Cross-Sectional and Longitudinal Association of Clinical and Neurocognitive Factors With Apathy in Patients With Parkinson Disease.

BACKGROUND AND OBJECTIVES: A robust understanding of the natural history of apathy in Parkinson disease (PD) is foundational for developing effective clinical management tools. However, large longitudinal studies are lacking while the literature is inconsistent about even cross-sectional associations. We aimed to determine the longitudinal predictors of apathy development in a large cohort of people with PD and its cross-sectional associations and trajectories over time, using sophisticated Bayesian modeling techniques. METHODS: People with PD followed up in the longitudinal New Zealand Parkinson's progression project were included. Apathy was defined using the neuropsychiatric inventory subscale ≥4, and analyses were also repeated using a less stringent cutoff of ≥1. Both MoCA and comprehensive neuropsychological testing were used as appropriate to the model. Depression was assessed using the hospital anxiety and depression scale. Cross-sectional Bayesian regressions were conducted, and a multistate predictive model was used to identify factors that predict the initial onset of apathy in nonapathetic PD, while also accounting for the competing risk of death. The relationship between apathy presence and mortality was also investigated. RESULTS: Three hundred forty-six people with PD followed up for up to 14 years across a total of 1,392 sessions were included. Apathy occurrence did not vary significantly across the disease course (disease duration odds ratio [OR] = 0.55, [95% CI 0.28-1.12], affecting approximately 11% or 22% of people at any time depending on the NPI cutoff used. Its presence was associated with a significantly higher risk of death after controlling for all other factors (hazard ratio [HR] = 2.92 [1.50-5.66]). Lower cognition, higher depression levels, and greater motor severity predicted apathy development in those without motivational deficits (HR [cognition] = 0.66 [0.48-0.90], HR [depression] = 1.45 [1.04-2.02], HR [motor severity] = 1.37 [1.01-1.86]). Cognition and depression were also associated with apathy cross-sectionally, along with male sex and possibly lower dopaminergic therapy level, but apathy still occurred across the full spectrum of each variable (OR [cognition] = 0.58 [0.44-0.76], OR [depression] = 1.43 [1.04-1.97], OR [female sex] = 0.45 [0.22-0.92], and OR [levodopa equivalent dose] = 0.78 [0.59-1.04]. DISCUSSION: Apathy occurs across the PD time course and is associated with higher mortality. Depressive symptoms and cognitive impairment in particular predict its future development in those with normal motivation.

Multiscale Modes of Functional Brain Connectivity.

Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10-30 distributed modes or parcellations with 100-1000 localised parcels, both missing across-scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict ~900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a paradigm shift in functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.

Electrospinning polymersomes into bead-on-string polyethylene oxide fibres for the delivery of biopharmaceuticals to mucosal epithelia.

Fibrous mucoadhesive polymer membranes prepared using electrospinning demonstrate many advantages for mucosal drug delivery compared to other formulations. Previous electrospun membrane formulations have been developed mainly for the delivery of small molecule drugs. There remains great potential to further develop the technology for the delivery of vesicular vectors that allow administration of advanced therapeutic agents. However, there are no previous reports demonstrating the release of intact drug delivery vesicles from electrospun materials. Here, we describe incorporation and release of protein-loaded polymersomes from polyethylene oxide (PEO)-based electrospun membranes. Polymersomes comprising a copolymer of glycerol monomethacrylate (GMA) and hydroxypropyl methacrylate (HPMA) were prepared using polymerization-induced self-assembly and incorporated within PEO membranes using bead-on-string electrospinning at approximately 40 % w/w by polymer mass. Super-resolution fluorescence imaging showed that the vesicles remained intact and retained their encapsulated protein load within the fibre beads. Transmission electron microscopy and dynamic light scattering demonstrated that polymersomes retained their morphology following release from the polymer fibres. F(ab) antibody fragments were encapsulated within polymersomes and then electrospun into membranes. 78 ± 13 % of the F(ab) remained encapsulated within polymersomes during electrospinning and retained functionality when released from electrospun membranes, demonstrating that the formulation is suitable for the delivery of biologics. Membranes were non-irritant to the oral epithelium and fluorescence microscopy detected accumulation of polymersomes within the epithelia following application. This innovative drug delivery approach represents a novel and potentially highly useful method for the administration of large molecular mass therapeutic molecules to diseased mucosal sites.

Altered nucleus accumbens functional connectivity precedes apathy in Parkinson's disease.

Work in animal and human neuroscience has identified neural regions forming a network involved in the production of motivated, goal-directed behaviour. In particular, the nucleus accumbens and anterior cingulate cortex are recognized as key network nodes underlying decisions of whether to exert effort for reward, to drive behaviour. Previous work has convincingly shown that this cognitive mechanism, known as effort-based decision making, is altered in people with Parkinson's disease with a syndrome of reduced goal-directed behaviour-apathy. Building on this work, we investigated whether the neural regions implementing effort-based decision-making were associated with apathy in Parkinson's disease, and more importantly, whether changes to these regions were evident prior to apathy development. We performed a large, multimodal neuroimaging analysis in a cohort of people with Parkinson's disease (n = 199) with and without apathy at baseline. All participants had ∼2-year follow-up apathy scores, enabling examination of brain structure and function specifically in those with normal motivation who converted to apathy by ∼2-year follow-up. In addition, of the people with normal motivation, a subset (n = 56) had follow-up neuroimaging data, allowing for examination of the 'rate of change' in key nodes over time in those who did, and did not, convert to apathy. Healthy control (n = 54) data were also included to aid interpretation of findings. Functional connectivity between the nucleus accumbens and dorsal anterior cingulate cortex was higher in people with normal motivation who later converted to apathy compared to those who did not, whereas no structural differences were evident between these groups. In contrast, grey matter volume in these regions was reduced in the group with existing apathy. Furthermore, of those with normal motivation who had undergone longitudinal neuroimaging, converters to apathy showed a higher rate of change in grey matter volume within the nucleus accumbens. Overall, we show that changes in functional connectivity between nucleus accumbens and anterior cingulate cortex precedes apathy in people with Parkinson's disease, with conversion to apathy associated with higher rate of grey matter volume loss in nucleus accumbens, despite no baseline differences. These findings significantly add to an accumulating body of transdiagnostic evidence that apathy arises from disruption to key nodes within a network in which normal goal-directed behaviour is instantiated, and raise the possibility of identifying those at risk for developing apathy before overt motivational deficits have arisen.

Comparison between gradients and parcellations for functional connectivity prediction of behavior.

Resting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average "hard" parcellations (Schaefer et al., 2018), individual-specific "hard" parcellations (Kong et al., 2021a), and an individual-specific "soft" parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average "hard" parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.

Predicting future depressive episodes from resting-state fMRI with generative embedding.

After a first episode of major depressive disorder (MDD), there is substantial risk for a long-term remitting-relapsing course. Prevention and early interventions are thus critically important. Various studies have examined the feasibility of detecting at-risk individuals based on out-of-sample predictions about the future occurrence of depression. However, functional magnetic resonance imaging (fMRI) has received very little attention for this purpose so far. Here, we explored the utility of generative models (i.e. different dynamic causal models, DCMs) as well as functional connectivity (FC) for predicting future episodes of depression in never-depressed adults, using a large dataset (N = 906) of task-free ("resting state") fMRI data from the UK Biobank (UKB). Connectivity analyses were conducted using timeseries from pre-computed spatially independent components of different dimensionalities. Over a three-year period, 50% of selected participants showed indications of at least one depressive episode, while the other 50% did not. Using nested cross-validation for training and a held-out test set (80/20 split), we systematically examined the combination of 8 connectivity feature sets and 17 classifiers. We found that a generative embedding procedure based on combining regression DCM (rDCM) with a support vector machine (SVM) enabled the best predictions, both on the training set (0.63 accuracy, 0.66 area under the curve, AUC) and the test set (0.62 accuracy, 0.64 AUC; p < 0.001). However, on the test set, rDCM was only slightly superior to predictions based on FC (0.59 accuracy, 0.61 AUC). Interpreting model predictions based on SHAP (SHapley Additive exPlanations) values suggested that the most predictive connections were widely distributed and not confined to specific networks. Overall, our analyses suggest (i) ways of improving future fMRI-based generative embedding approaches for the early detection of individuals at-risk for depression and that (ii) achieving accuracies of clinical utility may require combination of fMRI with other data modalities.

Predicting accidental release of engineered nanomaterials to the environment.

Challenges in distinguishing between natural and engineered nanomaterials (ENMs) and the lack of historical records on ENM accidents have hampered attempts to estimate the accidental release and associated environmental impacts of ENMs. Building on knowledge from the nuclear power industry, we provide an assessment of the likelihood of accidental release rates of ENMs within the next 10 and 30 years. We evaluate risk predictive methodology and compare the results with empirical evidence, which enables us to propose modelling approaches to estimate accidental release risk probabilities. Results from two independent modelling approaches based on either assigning 0.5% of reported accidents to ENM-releasing accidents (M1) or based on an evaluation of expert opinions (M2) correlate well and predict severe accidental release of 7% (M1) in the next 10 years and of 10% and 20% for M2 and M1, respectively, in the next 30 years. We discuss the relevance of these results in a regulatory context.

Assessing the relationship between gravidity and placental malaria among pregnant women in a high transmission area in Ghana.

BACKGROUND: Malaria infection during pregnancy can cause significant morbidity and mortality to a pregnant woman, her fetus and newborn. In areas of high endemic transmission, gravidity is an important risk factor for infection, but there is a complex relationship with other exposure-related factors, and use of protective measures. This study investigated the association between gravidity and placental malaria (PM), among pregnant women aged 14-49 in Kintampo, a high transmission area of Ghana. METHODS: Between 2008 and 2011, as part of a study investigating the association between PM and malaria in infancy, pregnant women attending antenatal care (ANC) clinics in the study area were enrolled and followed up until delivery. The outcome of PM was assessed at delivery by placental histopathology. Multivariable logistic regression analyses were used to investigate the association between gravidity and PM, identify other key risk factors, and control for potential confounders. Pre-specified effect modifiers including area of residence, socio-economic score (SES), ITN use and IPTp-SP use were explored. RESULTS: The prevalence of PM was 65.9% in primigravidae, and 26.5% in multigravidae. After adjusting for age, SES and relationship status, primigravidae were shown to have over three times the odds of PM compared to multigravidae, defined as women with 2 or more previous pregnancies [adjusted OR = 3.36 (95% CI 2.39-4.71), N = 1808, P < 0.001]. The association appeared stronger in rural areas [OR for PG vs. MG was 3.79 (95% CI 3.61-5.51) in rural areas; 2.09 (95% CI 1.17-3.71) in urban areas; P for interaction = 0.07], and among women with lower socio-economic scores [OR for PG vs. MG was 4.73 (95% CI 3.08-7.25) amongst women with lower SES; OR = 2.14 (95% CI 1.38-3.35) among women with higher SES; P for interaction = 0.008]. There was also evidence of lower risk among primigravidae with better use of the current preventive measures IPTp and LLIN. CONCLUSIONS: The burden of PM is most heavily focused on primigravidae of low SES living in rural areas of high transmission. Programmes should prioritize primigravidae and young women of child-bearing age for interventions such as LLIN distribution, educational initiatives and treatment to reduce the burden of malaria in first pregnancy.

Determinants of COVID-19 vaccine acceptance and hesitancy among healthcare professionals in the Kintampo North Municipality, Bono East Region, Ghana.

Objectives: To assess the determinants of COVID-19 vaccine acceptance and hesitation among Health Care Professionals (HCPs) in the Kintampo North Municipality of Ghana. Design: An analytical cross-sectional study. Setting: The study was carried out in the Kintampo North Municipality. Participants: All health care professionals within the Kintampo North Municipality of Ghana. Main outcome measure: Acceptance of COVID-19 vaccine. Results: In all, 215 HCPs were included in this study. The overall vaccine acceptance was 78.6% among HCPs, while 21.4% were hesitant to receive the COVID-19 vaccine. Majority (57.7%) of HCPs believed that COVID-19 vaccines were safe. The following factors were found to influence vaccine acceptance significantly; those who knew someone who has taken the vaccine (adjusted Odds Ratio [aOR]; 14.9, 95% Confidence Interval [95% CI];5.0-45.0, p<0.001), those who think COVID -19 vaccine in Ghana was safe (AOR;9.2, 95%CI;3.3-25.8, P<0.001), those who said vaccines are effective in controlling COVID-19 transmission (aOR=5.0, 95%CI;2.1-12.4, p<0.001), and those who have never refused vaccines in the past (aOR=7.8, 95CI;1.6-37.8, p=0.01). Conclusion: The study indicated high COVID-19 vaccination acceptability among HCPs. However, some HCPs are hesitant to take COVID-19 vaccinations immediately. Increased adoption of COVID-19 vaccinations among HCPs and the broader Ghanaian population requires concerted efforts, including strengthening public health education on the perceived risks and safety of COVID-19 vaccines. Funding: None declared.

Determinants of COVID-19 vaccine acceptance and hesitancy among healthcare professionals in the Kintampo North Municipality, Bono East Region, Ghana

tives: To assess the determinants of COVID-19 vaccine acceptance and hesitation among Health Care Professionals (HCPs) in the Kintampo North Municipality of Ghana. Design: An analytical cross-sectional study. Setting: The study was carried out in the Kintampo North Municipality. Participants: All health care professionals within the Kintampo North Municipality of Ghana. Main outcome measure: Acceptance of COVID-19 vaccine. Results: In all, 215 HCPs were included in this study. The overall vaccine acceptance was 78.6% among HCPs, while 21.4% were hesitant to receive the COVID-19 vaccine. Majority (57.7%) of HCPs believed that COVID-19 vaccines were safe. The following factors were found to influence vaccine acceptance significantly; those who knew someone who has taken the vaccine (adjusted Odds Ratio [aOR]; 14.9, 95% Confidence Interval [95% CI];5.0-45.0, p<0.001), those who think COVID -19 vaccine in Ghana was safe (AOR;9.2, 95%CI;3.3-25.8, P<0.001), those who said vaccines are effective in controlling COVID-19 transmission (aOR=5.0, 95%CI;2.1-12.4, p<0.001), and those who have never refused vaccines in the past (aOR=7.8, 95CI;1.6-37.8, p=0.01). Conclusion: The study indicated high COVID-19 vaccination acceptability among HCPs. However, some HCPs are hesitant to take COVID-19 vaccinations immediately. Increased adoption of COVID-19 vaccinations among HCPs and the broader Ghanaian population requires concerted efforts, including strengthening public health education on the perceived risks and safety of COVID-19 vaccines

Interoception of breathing and its relationship with anxiety.

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. Although interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modeling and high-field (7 T) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability. Notably, the anterior insula was associated with both breathing-related prediction certainty and prediction errors, suggesting an important role in representing and updating models of the body. Individuals with low versus moderate anxiety traits showed differential anterior insula activity for prediction certainty. Multi-modal analyses of data from fMRI, computational assessments of breathing-related metacognition, and questionnaires demonstrated that anxiety-interoception links span all levels from perceptual sensitivity to metacognition, with strong effects seen at higher levels of interoceptive processes.

The Filter Detection Task for measurement of breathing-related interoception and metacognition.

The study of the brain's processing of sensory inputs from within the body ('interoception') has been gaining rapid popularity in neuroscience, where interoceptive disturbances are thought to exist across a wide range of chronic physiological and psychological conditions. Here we present a task and analysis procedure to quantify specific dimensions of breathing-related interoception, including interoceptive sensitivity, decision bias, metacognitive bias, and metacognitive performance. Two major developments address some of the challenges presented by low trial numbers in interoceptive experiments: (i) a novel adaptive algorithm to maintain task performance at 70-75% accuracy; (ii) an extended hierarchical metacognitive model to estimate regression parameters linking metacognitive performance to relevant (e.g. clinical) variables. We demonstrate the utility of the task and analysis developments, using both simulated data and three empirical datasets. This methodology represents an important step towards accurately quantifying interoceptive dimensions from a simple experimental procedure that is compatible with clinical settings.

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data.

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

Regression dynamic causal modeling for resting-state fMRI.

"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI-regression dynamic causal modeling (rDCM)-extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

Ibrutinib for mantle cell lymphoma at first relapse: a United Kingdom real-world analysis of outcomes in 211 patients.

Ibrutinib is an established treatment for relapsed/refractory (R/R) mantle cell lymphoma (MCL) and clinical trial data supports use at second line compared to later relapse. We aimed to investigate outcomes and tolerability for ibrutinib when given second line in a real-world setting. Our multicentre retrospective analysis included 211 R/R MCL patients, median age 73 years, receiving ibrutinib second-line within the United Kingdom's National Health Service. Overall response to ibrutinib was 69% (complete response 27%). The median progression-free survival (PFS) was 17·8 months (95% CI 13·1-22·2) and median overall survival (OS) 23·9 months (95% CI 15·0-32·8). Drug-related adverse event led to dose reduction in 10% of patients and discontinuation in 5%. In patients with progressive disease, accounting for 100 of 152 patients stopping ibrutinib, 43% received further systemic therapy. Post-ibrutinib rituximab, bendamustine and cytarabine (R-BAC) showed a trend toward improved survival compared to alternative systemic treatments (post-ibrutinib median OS 14·0 months, 95% CI 8·1-19·8, vs. 3·6 months, 95% CI 2·6-4·5, P = 0·06). Our study confirms the clinical benefit and good tolerability of ibrutinib at first relapse in a real-world population. Patients progressing on ibrutinib had limited survival but outcomes with R-BAC in select patients were promising.

A Hilbert-based method for processing respiratory timeseries.

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators. Finally, we demonstrate that this leads to an increase in the amount of respiration-related variance removed from fMRI data when used as part of a typical preprocessing pipeline. Our implementation is publicly available as part of the PhysIO package, which is distributed as part of the open-source TAPAS toolbox (https://translationalneuromodeling.org/tapas).

Qualitative and quantitative vibrational spectroscopic analysis of macronutrients in breast milk.

Raman and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy were used to analyze 208 breast milk samples as part of a larger research study. Comprehensive qualitative and quantitative analysis was carried out using chemometric methods: principal component analysis (PCA) and partial least squares (PLS) regression. The obtained information on the main macronutrients (protein, fat and carbohydrate) were primarily evaluated in relation to the available metadata of the samples, where study location and respective primary food sources revealed a stronger differentiation in fat composition than its absolute content. The limitations and challenges of using both spectroscopic techniques for the type of analysis are also highlighted.

Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning.

Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain. These findings suggested selective dopaminergic and cholinergic influences on precision-weighted PEs at different hierarchical levels. Here, we tested this hypothesis, repeating our fMRI study under pharmacological manipulations in healthy participants. Specifically, we performed two pharmacological fMRI studies with a between-subject double-blind placebo-controlled design: study 1 used antagonists of dopaminergic (amisulpride) and muscarinic (biperiden) receptors, study 2 used enhancing drugs of dopaminergic (levodopa) and cholinergic (galantamine) modulation. Pooled across all pharmacological conditions of study 1 and study 2, respectively, we found that low-level precision-weighted PEs activated the midbrain and high-level precision-weighted PEs the basal forebrain as in our previous study. However, we found pharmacological effects on brain activity associated with these computational quantities only when splitting the precision-weighted PEs into their PE and precision components: in a brainstem region putatively containing cholinergic (pedunculopontine and laterodorsal tegmental) nuclei, biperiden (compared to placebo) enhanced low-level PE responses and attenuated high-level PE activity, while amisulpride reduced high-level PE responses. Additionally, in the putative dopaminergic midbrain, galantamine compared to placebo enhanced low-level PE responses (in a body-weight dependent manner) and amisulpride enhanced high-level precision activity. Task behaviour was not affected by any of the drugs. These results do not support our hypothesis of a clear-cut dichotomy between different hierarchical inference levels and neurotransmitter systems, but suggest a more complex interaction between these neuromodulatory systems and hierarchical Bayesian quantities. However, our present results may have been affected by confounds inherent to pharmacological fMRI. We discuss these confounds and outline improved experimental tests for the future.

Challenges and future directions for representations of functional brain organization.

A key principle of brain organization is the functional integration of brain regions into interconnected networks. Functional MRI scans acquired at rest offer insights into functional integration via patterns of coherent fluctuations in spontaneous activity, known as functional connectivity. These patterns have been studied intensively and have been linked to cognition and disease. However, the field is fractionated. Diverging analysis approaches have segregated the community into research silos, limiting the replication and clinical translation of findings. A primary source of this fractionation is the diversity of approaches used to reduce complex brain data into a lower-dimensional set of features for analysis and interpretation, which we refer to as brain representations. In this Primer, we provide an overview of different brain representations, lay out the challenges that have led to the fractionation of the field and that continue to form obstacles for convergence, and propose concrete guidelines to unite the field.