357 research outputs found
A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB
This tutorial provides a worked example of using Dynamic Causal Modelling
(DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject
variability in neural circuitry (effective connectivity). This involves
specifying a hierarchical model with two or more levels. At the first level,
state space models (DCMs) are used to infer the effective connectivity that
best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG).
Subject-specific connectivity parameters are then taken to the group level,
where they are modelled using a General Linear Model (GLM) that partitions
between-subject variability into designed effects and additive random effects.
The ensuing (Bayesian) hierarchical model conveys both the estimated connection
strengths and their uncertainty (i.e., posterior covariance) from the subject
to the group level; enabling hypotheses to be tested about the commonalities
and differences across subjects. This approach can also finesse parameter
estimation at the subject level, by using the group-level parameters as
empirical priors. We walk through this approach in detail, using data from a
published fMRI experiment that characterised individual differences in
hemispheric lateralization in a semantic processing task. The preliminary
subject specific DCM analysis is covered in detail in a companion paper. This
tutorial is accompanied by the example dataset and step-by-step instructions to
reproduce the analyses
Linking fast and slow: the case for generative models
A pervasive challenge in neuroscience is testing whether neuronal
connectivity changes over time due to specific causes, such as stimuli, events,
or clinical interventions. Recent hardware innovations and falling data storage
costs enable longer, more naturalistic neuronal recordings. The implicit
opportunity for understanding the self-organised brain calls for new analysis
methods that link temporal scales: from the order of milliseconds over which
neuronal dynamics evolve, to the order of minutes, days or even years over
which experimental observations unfold. This review article demonstrates how
hierarchical generative models and Bayesian inference help to characterise
neuronal activity across different time scales. Crucially, these methods go
beyond describing statistical associations among observations and enable
inference about underlying mechanisms. We offer an overview of fundamental
concepts in state-space modeling and suggest a taxonomy for these methods.
Additionally, we introduce key mathematical principles that underscore a
separation of temporal scales, such as the slaving principle, and review
Bayesian methods that are being used to test hypotheses about the brain with
multi-scale data. We hope that this review will serve as a useful primer for
experimental and computational neuroscientists on the state of the art and
current directions of travel in the complex systems modelling literature.Comment: 20 pages, 5 figure
Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling
INTRODUCTION: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data. METHODS: A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation. RESULTS: Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values. CONCLUSION: DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment
Variational representational similarity analysis
© 2019 The Authors This technical note describes a variational or Bayesian implementation of representational similarity analysis (RSA) and pattern component modelling (PCM). It considers RSA and PCM as Bayesian model comparison procedures that assess the evidence for stimulus or condition-specific patterns of responses distributed over voxels or channels. On this view, one can use standard variational inference procedures to quantify the contributions of particular patterns to the data, by evaluating second-order parameters or hyperparameters. Crucially, this allows one to use parametric empirical Bayes (PEB) to infer which patterns are consistent among subjects. At the between-subject level, one can then assess the evidence for different (combinations of) hypotheses about condition-specific effects using Bayesian model comparison. Alternatively, one can select a single hypothesis that best explains the pattern of responses using Bayesian model selection. This note rehearses the technical aspects of within and between-subject RSA using a worked example, as implemented in the Statistical Parametric Mapping (SPM) software. En route, we highlight the connection between univariate and multivariate analyses of neuroimaging data and the sorts of analyses that are possible using component modelling and representational similarity analysis
Neurovascular coupling: insights from multi-modal dynamic causal modelling of fMRI and MEG
This technical note presents a framework for investigating the underlying
mechanisms of neurovascular coupling in the human brain using multi-modal
magnetoencephalography (MEG) and functional magnetic resonance (fMRI)
neuroimaging data. This amounts to estimating the evidence for several
biologically informed models of neurovascular coupling using variational
Bayesian methods and selecting the most plausible explanation using Bayesian
model comparison. First, fMRI data is used to localise active neuronal sources.
The coordinates of neuronal sources are then used as priors in the
specification of a DCM for MEG, in order to estimate the underlying generators
of the electrophysiological responses. The ensuing estimates of neuronal
parameters are used to generate neuronal drive functions, which model the pre
or post synaptic responses to each experimental condition in the fMRI paradigm.
These functions form the input to a model of neurovascular coupling, the
parameters of which are estimated from the fMRI data. This establishes a
Bayesian fusion technique that characterises the BOLD response - asking, for
example, whether instantaneous or delayed pre or post synaptic signals mediate
haemodynamic responses. Bayesian model comparison is used to identify the most
plausible hypotheses about the causes of the multimodal data. We illustrate
this procedure by comparing a set of models of a single-subject auditory fMRI
and MEG dataset. Our exemplar analysis suggests that the origin of the BOLD
signal is mediated instantaneously by intrinsic neuronal dynamics and that
neurovascular coupling mechanisms are region-specific. The code and example
dataset associated with this technical note are available through the
statistical parametric mapping (SPM) software package
Identifying Abnormal Connectivity in Patients Using Dynamic Causal Modeling of fMRI Responses
Functional imaging studies of brain damaged patients offer a unique opportunity to understand how sensorimotor and cognitive tasks can be carried out when parts of the neural system that support normal performance are no longer available. In addition to knowing which regions a patient activates, we also need to know how these regions interact with one another, and how these inter-regional interactions deviate from normal. Dynamic causal modeling (DCM) offers the opportunity to assess task-dependent interactions within a set of regions. Here we review its use in patients when the question of interest concerns the characterization of abnormal connectivity for a given pathology. We describe the currently available implementations of DCM for fMRI responses, varying from the deterministic bilinear models with one-state equation to the stochastic non-linear models with two-state equations. We also highlight the importance of the new Bayesian model selection and averaging tools that allow different plausible models to be compared at the single subject and group level. These procedures allow inferences to be made at different levels of model selection, from features (model families) to connectivity parameters. Following a critical review of previous DCM studies that investigated abnormal connectivity we propose a systematic procedure that will ensure more flexibility and efficiency when using DCM in patients. Finally, some practical and methodological issues crucial for interpreting or generalizing DCM findings in patients are discussed
A tutorial on group effective connectivity analysis, part 1: first level analysis with DCM for fMRI
Dynamic Causal Modelling (DCM) is the predominant method for inferring
effective connectivity from neuroimaging data. In the 15 years since its
introduction, the neural models and statistical routines in DCM have developed
in parallel, driven by the needs of researchers in cognitive and clinical
neuroscience. In this tutorial, we step through an exemplar fMRI analysis in
detail, reviewing the current implementation of DCM and demonstrating recent
developments in group-level connectivity analysis. In the first part of the
tutorial (current paper), we focus on issues specific to DCM for fMRI,
unpacking the relevant theory and highlighting practical considerations. In
particular, we clarify the assumptions (i.e., priors) used in DCM for fMRI and
how to interpret the model parameters. This tutorial is accompanied by all the
necessary data and instructions to reproduce the analyses using the SPM
software. In the second part (in a companion paper), we move from subject-level
to group-level modelling using the Parametric Empirical Bayes framework, and
illustrate how to test for commonalities and differences in effective
connectivity across subjects, based on imaging data from any modality
The Glycerol-Dependent Metabolic Persistence of Pseudomonas putida KT2440 Reflects the Regulatory Logic of the GlpR Repressor
The growth of the soil bacterium Pseudomonas putida KT2440 on glycerol as the sole carbon source is characterized by a prolonged lag phase, not observed with other carbon substrates. We examined the bacterial growth in glycerol cultures while monitoring the metabolic activity of individual cells. Fluorescence microscopy and flow cytometry, as well as the analysis of the temporal start of growth in single-cell cultures, revealed that adoption of a glycerol-metabolizing regime was not the result of a gradual change in the whole population but rather reflected a time-dependent bimodal switch between metabolically inactive (i.e., nongrowing) and fully active (i.e., growing) bacteria. A transcriptional Φ(glpD-gfp) fusion (a proxy of the glycerol-3-phosphate [G3P] dehydrogenase activity) linked the macroscopic phenotype to the expression of the glp genes. Either deleting glpR (encoding the G3P-responsive transcriptional repressor that controls the expression of the glpFKRD gene cluster) or altering G3P formation (by overexpressing glpK, encoding glycerol kinase) abolished the bimodal glpD expression. These manipulations eliminated the stochastic growth start by shortening the otherwise long lag phase. Provision of glpR in trans restored the phenotypes lost in the ΔglpR mutant. The prolonged nongrowth regime of P. putida on glycerol could thus be traced to the regulatory device controlling the transcription of the glp genes. Since the physiological agonist of GlpR is G3P, the arrangement of metabolic and regulatory components at this checkpoint merges a positive feedback loop with a nonlinear transcriptional response, a layout fostering the observed time-dependent shift between two alternative physiological states
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