Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models

Bayesian deconvolution for computational cognitive modeling of fMRI data

A central goal of cognitive neuroscience is to make inferences about underlying cognitive processes from observable data. However, current fMRI analysis tools cannot directly estimate latent parameters in computational cognitive models from blood-oxygen-level-dependent (BOLD) signal. Here, we present a novel Bayesian deconvolution technique for full hierarchical generative cognitive modeling of fMRI timeseries data. We validated this approach by applying Bayesian deconvolution to the monetary incentive delay (MID) task to identify processes underlying incentive anticipation in a sample of 54 individuals who underwent 2 scan sessions as part of a clinical trial for anxiety and depression. Based on a series of Bayesian models, we found evidence that striatal reward region activity reflects incentive prediction error rather than raw incentive value during anticipation of monetary loss or gain. Test-retest analyses found that individual parameters estimated using a generative Bayesian learning model (including a persistent prior parameter and a β parameter representing a scaling term between prediction error and BOLD signal) were estimated more reliably than an index derived from traditional fMRI analysis (beta value for contrast between gain and no gain during anticipation). Our method holds potential for broad application to diverse neural processes and individual differences in health and disease.

Lung cancer incidence and mortality in trend and prediction between 2012-2030 in Shandong Province, using a Bayesian age-period-cohort model

Objectives

Lung cancer is one of the most common cancers in Shandong Province, China. Projecting future cancer trend is crucial for planning cancer control. We aimed to examine the trend of lung cancer incidence and mortality from 2012 to 2023, and predict the lung cancer burden to 2030 in Shandong.

Methods

Data of lung cancer incidence and mortality from 2012 to 2023 were obtained from the Shandong Cancer Registries. The average annual percentage change (AAPC) was used to quantify the trend of the lung cancer age-standardised rate using Joinpoint software. Bayesian age-period-cohort model was used to predict lung cancer incidence and mortality from 2024 to 2030.

Results

The age-standardised incidence rate (ASIR) remained stable from 2012 to 2023. The ASIR in males decreased with an AAPC of -1.350%, while the ASIR in females increased with an AAPC of 2.429%. The age-standardised mortality rate (ASMR) decreased with an AAPC of -2.911%. This trend was also observed in males (AAPC=-2.513%), females (AAPC=-3.632%), urban areas (AAPC=-3.267%) and rural areas (AAPC=-2.603%). For our predictions, the ASIR will increase to 49.21 per 100,000 until 2030, with an AAPC of 1.873%. This upward trend is expected for females and urban areas, with an AAPC of 4.496% and 4.176%, while it is not observed for males and rural areas. The ASMR is expected to remain stable up to 2030, and this trend will maintain both in males and females. The ASMR will exhibit an upward trend (AAPC=1.100%) in urban areas and a downward trend (AAPC=-0.915%) in rural areas.

Conclusion

The ASIR of lung cancer will increase until 2030, while the ASMR of lung cancer is expected to remain stable in Shandong. It is necessary to take further preventive measures such as strengthening tobacco control, enhancing health education and expanding screening efforts.

Global burden and prediction study of cutaneous squamous cell carcinoma from 1990 to 2030: A systematic analysis and comparison with China

Background

China has the highest number of new cancer cases and deaths globally. Due to particularly low scores in health care quality for cutaneous squamous cell carcinoma (cSCC), the country's cSCC burden requires greater awareness. Consequently, we aimed to evaluate and predict the trend of the cSCC burden globally and in China from 1990 to 2030.

Methods

We retrieved data from the Global Burden of Disease 2019 study, which provided estimates of the incidence, mortality, prevalence, and disability-adjusted life years (DALYs) of cSCC from 1990 to 2019. We set up joint-point analyses and Bayesian age-period-cohort (BAPC) models to predict the disease burden of cSCC up to 2030.

Results

In 2019, China reported age-standardised rates of cSCC prevalence, incidence, mortality, and DALYs of 2.54, 2.12, 0.88, and 16.76 per 100 000 population, respectively. The country's prevalence and incidence rates from 1990 to 2019 were lower than the global levels, but its mortality and DALY rates were higher. The age-standardised rates were higher for males, and the disease burden increased with each age group globally and in China. Moreover, the average annual percentage change showed all indicators were growing faster than the global levels. According to the BAPC model, there will be an upward trend in the prevalence and incidence globally and in China between 2020 and 2030, with a decrease in mortality and DALYs.

Conclusions

We observed an upward trend in the cSCC burden over the past 30 years in China. Prevalence and incidence are expected to continue at a higher rate than the global average in the next decade, while mortality and DALYs are predicted to decrease. As the Chinese population ages, efforts toward managing and preventing cSCC should be targeted towards the elderly population.

Attentional effects on local V1 microcircuits explain selective V1-V4 communication

Selective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might explain selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modelled induced responses with a Dynamic Causal Model for Cross-Spectral Densities and found that selective entrainment can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing.

A spectral sampling algorithm in dynamic causal modelling for resting-state fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely utilized to study the directed influences among neural populations which were called effective connectivity (EC), and the spectral dynamic causal modelling (spDCM) is the state-of-the-art framework to identify them. However, spDCM used variational Laplace to approximate the posterior density by maximizing the free energy, which might underestimate the variability of posterior density and get locked to the local minima. A spectral sampling algorithm (SS-DCM) was proposed to improve the estimation accuracy of the dynamic causal model for rs-fMRI. In SS-DCM, a naïve Bayesian model was constructed in the spectral domain, which described the probabilistic relationship between the sampled parameters and cross spectra of the observed blood oxygen level-dependent signals, and the parameters were sampled using randomly walked Markov Chain Monto Carlo scheme. The root mean square errors of the estimation of EC and hemodynamic parameters of SS-DCM, spDCM and generalized filter scheme were compared in the synthetic data, and SS-DCM was the most accurate and stable. A comparative evaluation using empirical rs-fMRI data was performed to study the EC pattern of the default mode network and compare the accuracy of classification between typically developed subjects and inattentive attention deficit and hyperactivity disorder patients. The results showed high consistency of positivity and negativity of EC between spDCM and SS-DCM, and SS-DCM also provided higher classification accuracy. It is highlighted that SS-DCM improves the accuracy of the estimation of EC and provides accurate information of discrepancies between diseased and healthy subjects using rs-fMRI.

Trends and predictions of lung cancer incidence in Jiangsu Province, China, 2009-2030: a bayesian age-period-cohort modelling study

BACKGROUND

Lung cancer is currently the most frequent cancer in Jiangsu Province, China, and the features of cancer distribution have changed continuously in the last decade. The aim of this study was to analyse the trend of the incidence of lung cancer in Jiangsu from 2009 to 2018 and predict the incidence from 2019 to 2030.

METHODS

Data on lung cancer incidence in Jiangsu from 2009 to 2018 were retrieved from the Jiangsu Cancer Registry. The average annual percentage change (AAPC) was used to quantify the trend of the lung cancer age-standardized rate (ASR) using Joinpoint software. Bayesian age-period-cohort models were used to predict lung cancer incidence up to 2030.

RESULTS

In Jiangsu, the lung cancer crude rate increased from 45.73 per 100,000 in 2009 to 69.93 per 100,000 in 2018. The lung cancer ASR increased from 29.03 per 100,000 to 34.22 per 100,000 during the same period (AAPC = 2.17%, 95% confidence interval [CI], 1.54%, 2.80%). Between 2019 and 2030, the lung cancer ASR is predicted to decrease slightly to 32.14 per 100,000 (95% highest density interval [HDI], 24.99, 40.22). Meanwhile, the ASR showed a downward trend in males and rural regions while remaining stable in females and urban regions.

CONCLUSION

We predict that the incidence of lung cancer in Jiangsu will decrease in the next 12 years, mainly due to the decrease in males and rural areas. Therefore, future lung cancer prevention and control efforts should be focused on females and urban regions.

Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models

Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive psychology, and treatment follow-up. The Hopkins Verbal Learning Test (HVLT) is a general verbal knowledge and memory assessment administered repeatedly as part of a neurophysiological experiment to examine an individual's performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via the hybrid Hamiltonian Monte Carlo (HMC) under the Bayesian Regression Models using 'Stan' (BRMS) package of R. Comparisons of the fitted models were done using leave-one-out information criteria (LOO-CV), Widely applicable information criterion (WAIC), and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests [from the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study dataset] using the hybrid Hamiltonian-Markov Chain Monte Carlo approach.

An introduction to thermodynamic integration and application to dynamic causal models

In generative modeling of neuroimaging data, such as dynamic causal modeling (DCM), one typically considers several alternative models, either to determine the most plausible explanation for observed data (Bayesian model selection) or to account for model uncertainty (Bayesian model averaging). Both procedures rest on estimates of the model evidence, a principled trade-off between model accuracy and complexity. In the context of DCM, the log evidence is usually approximated using variational Bayes. Although this approach is highly efficient, it makes distributional assumptions and is vulnerable to local extrema. This paper introduces the use of thermodynamic integration (TI) for Bayesian model selection and averaging in the context of DCM. TI is based on Markov chain Monte Carlo sampling which is asymptotically exact but orders of magnitude slower than variational Bayes. In this paper, we explain the theoretical foundations of TI, covering key concepts such as the free energy and its origins in statistical physics. Our aim is to convey an in-depth understanding of the method starting from its historical origin in statistical physics. In addition, we demonstrate the practical application of TI via a series of examples which serve to guide the user in applying this method. Furthermore, these examples demonstrate that, given an efficient implementation and hardware capable of parallel processing, the challenge of high computational demand can be overcome successfully. The TI implementation presented in this paper is freely available as part of the open source software TAPAS.

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.

Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models

In this article, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of latent parameters of subject‐wise generative models. Specifically, we focus on the case where the subject‐wise generative model is a dynamic causal model (DCM) for functional magnetic resonance imaging (fMRI) and clusters are defined in terms of effective brain connectivity. While an attractive approach for detecting mechanistically interpretable subgroups in heterogeneous populations, inverting such a hierarchical model represents a particularly challenging case, since DCM is often characterized by high posterior correlations between its parameters. In this context, standard MCMC schemes exhibit poor performance and extremely slow convergence. In this article, we investigate the properties of hierarchical clustering which lead to the observed failure of standard MCMC schemes and propose a solution designed to improve convergence but preserve computational complexity. Specifically, we introduce a class of proposal distributions which aims to capture the interdependencies between the parameters of the clustering and subject‐wise generative models and helps to reduce random walk behaviour of the MCMC scheme. Critically, these proposal distributions only introduce a single hyperparameter that needs to be tuned to achieve good performance. For validation, we apply our proposed solution to synthetic and real‐world datasets and also compare it, in terms of computational complexity and performance, to Hamiltonian Monte Carlo (HMC), a state‐of‐the‐art Monte Carlo technique. Our results indicate that, for the specific application domain considered here, our proposed solution shows good convergence performance and superior runtime compared to HMC.