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P3 Physical > Connectivity: Dynamic Causal Modelling (DCM) for fMRI > Flashcards

Connectivity: Dynamic Causal Modelling (DCM) for fMRI Flashcards

1
Q

What are the different type of brain connectivity?

A
  1. Anatomical/structural connectivity
  2. Functional connectivity
  3. Effective connectivity
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2
Q

What is used to measure axonal connections in animals?

A

Tract-tracing technique

In humans: only post-mortem processing of the tissue or Diffusion weighted imaging

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3
Q

What is functional connectivity an umbrella term for?

A

An approach that gives us an estimate of the statistical dependencies between regional time series
- It could be a correlation or derived from ICA

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4
Q

What is the difference between function and effective connectivity?

A

Functional connectivity is just descriptive - completely ignorant about how the dependencies arises

Effective connectivity - make a claim about the directed influences between neurons or neuronal populations

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5
Q

What is anatomical/structural connectivity?

A

Presence of axonal connections

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6
Q

What is the idea about Dynamic Casual Modelling (DCM)?

A

Dealing with a system where we cannot observe the state of interest directly e.g. neuronal activity be it in terms of membrane potential firing rate - it is hidden from us, we can only access it indirectly in terms of some observed measurements that could be a BOLD signal or electromagnetic potential distribution that is measured on the scalp level

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7
Q

What can be done in DCM?

A

Describe the hidden dynamics of a system in terms of differential equation that are parameterised e.g. theta

Incorporate some knowledge about design perturbation of the system e.g. sensory inputs that is administered as an experimental list

Also describe mathematically how any particular neuronal state translates into a measurement in terms of BOLD or electrophysiology

If we describe such a model for a given measurement –> invert the model, fit the model to the data, estimate the parameters and reconstruct their posterior distribution

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8
Q

What is an example of experiment done on the DCM?

A

Present visual stimuli and presented them in the visual hemi-field either left or right while the subject is fixated centrally

We are interested in a small system comprising the lingual gyrus on the left and right and the fusiform gyrus also on both hemispheres

We can model the neuronal dynamics in that system using a few simple assumptions and also knowledge we have about the brain

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9
Q

What is the anatomy of the visual system?

A

If we present the visual system in the periphery of the visual field it will arrive in the contralateral visual cortex

A stimulus in the right visual field will first be received by the left lingual gyrus

The regions are connected ti each other reciprocally within and across hemispheres

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10
Q

What are the two assumptions of DCM?

A
  1. All that matters that we are interested in can be summarised by a single number per region e.g. the mean activity in that region [population synaptic activity across neurons in the brain]
  2. Everything is linear
    - Write down equations for each of the areas
    - Each area is represented by one number
    - Describe the change in activity in area x1 as a linear combination of influences
    - The influence that x1 exerts onto itself [self-connection]
    - The influence that the second area exerts onto the first
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11
Q

What can the re-arrangement of equations give?

A

compact form by arranging terms into matrices and vectors

x = Ax + Cu

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12
Q

The matrices

A

1, Grouped the state changes into a vector [vector of dynamics]

  1. Grouped the coupling coefficients into a matrix which is the effective connectivity [endogenous connectivity]
  2. Have vector of system state
  3. Input parameters - describe how strongly the stimuli affect activity in the primary visual cortices
  4. Input functions ‘u’ - defined our stimuli

x= Ax + Cu

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13
Q

How can you model the changes in coupling strength by?

A

Slightly augmenting equations - how the context variable changes the two connections from the right to the left hemisphere

  • Mathematically express that by slightly augmenting the previous equations
  • if the contextual variable is off = 0 - everything is exactly the same as before
  • We are modelling additive changes in coupling strength as a function of our contextual variable
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14
Q

What does DCM model?

A

Additive changes in connection strength as a function of some controlled variable - some controlled task or context variable

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15
Q

What does DCM allow you to do?

A

Define a model that describes how several populations of neurons or regions interacts

  • First define the region of interest
  • Specify the connections between the regions based on understanding of the system
  • Specify where in that system perturbations enter [driving input] e.g. visual stimulations
  • Induces activity which propagates along connections defined
  • Specify which of these connections modulated in time by some controlled variables e.g. attention task

These are the 3 ingredients that map onto the 3 matrices

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16
Q

What are the 3 neural state equations?

A

A matrix - endogenous connectivity - connection strength per se

B matrices - one for each contextual variables that allow you to change temporally coupling strength

C matrices - encode strength of the driving or input perturbations

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17
Q

What happens if you integrate the equations?

A

Get time series for x - compare that to the real BOLD signal and based on the discrepancies you can make a choice of how you can update parameters and iterate through that procedure until you cannot further optimise the predictions

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18
Q

What gives an exponential function?

A

Integration of a first-order linear differential equation

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19
Q

What do Neural models enable us to make?

A

Inferences about brain circuitry using downstream measurements such as functional magnetic resonance imaging (fMRI)

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20
Q

What can neural models capture?

A

The mean activity of large numbers of neurons in a patch of brain tissue (Deco et al., 2008)

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21
Q

What is a common application of these models in Neuroimaging?

A

Assess effective connectivity - the directed casual influences among brain regions - or more simply the effect of one region on another

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22
Q

What is DCM?

A

A framework for specifying models of effective connectivity among brain regions, estimating parameters and testing hypothesis

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23
Q

What is DCM forward (generative) model?

A

Conceptualised as a procedure that generates neuroimaging timeseries from the underlying causes (e.g. neural fluctuations and connection strengths)

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24
Q

What does the generated timeseries depend on?

A

the model’s parameters, which generally have some useful interpretation; for example, a parameter may represent the strength of a particular neural connection

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25
Q

What happens after you have specified a forward model?

A

One can then stimulate data under different models (e.g. with different connectivity architectures) and ask which stimulation best characterises the observed data

  • model inversion (i.e. estimation) - process of finding the parameters that offer the best trade-off between accuracy and complexity of the model
  • Hypothesis are tested by comparing the evidence for different models (e.g. with different network architectures) either at the single-subject or the group level
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26
Q

How can you evaluate the evidence for a model?

A

One needs to average over the unknown parameters, which means model inversion is usually needed prior to model comparison

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27
Q

In the context of fMRI, what is the objective of DCM?

A

to explain the interactions among neural populations that show experimental effects. In other words, having identified where in the brain task-related effects are localised – usually using a mass-univariate (SPM) analysis – DCM is used to ask how those effects came about, in terms of (changes in) the underlying neural circuitry.

28
Q

What is neural activity tuned by?

A

A vector of parameters which includes the strength of connections and the extent to which the connections are influenced by experimental conditions

29
Q

What does the generated neural activity drive?

A

A model of neurovascular coupling and haemodynamics, which predicts the resulting change in blood volume and deoxyhaemoglobin level, tuned by the haemodynamics parameters

30
Q

What does the final part of the model predict?

A

fMRI timeseries including noise, that one would expect to measure given neural activity and haemodynamics

31
Q

What is DCM used to model?

A

connectivity between brain regions of interest (ROIs), and the criteria for defining ROIs varies across studies. For resting state experiments, there are no experimental effects, so ROIs are typically selected using an Independent Components Analysis (ICA), or using stereotaxic co-ordinates or masks from meta-analyses or the literature

32
Q

What does hypothesis determine?

A

determine the experimental design. An efficient design at the within-subject level typically involves varying at least two experimental factors independently. Commonly, one factor will be a manipulation of the stimuli that drive neural responses, and another factor will be a manipulation of the task demands or context that modulates these responses.

33
Q

What are the two types of experimental input that DCM distinguishes?

A
  1. Driving

2. Modulatory

34
Q

What are driving inputs?

A

Brief events that ‘ping’ specific regions in the neural network at the onset of each stimulus

35
Q

What regulates specific connections and represent the context in which the stimuli were presented?

A

Modulatory inputs up- or down

36
Q

What happens following experimental stimulation?

A

The temporal evolution of BOLD signal can be divided into deoxygenated, oxygenated and sustained response phase, each of which can be linked to interactions of neuronal activity, neurovascular coupling, and blood vessel dynamics

37
Q

What is the baseline level of BOLD signal determined by?

A

the net oxygen extraction exchange between neurons and blood vessels, as well as cerebral blood flow.

38
Q

What happens in response to experimental stimulations?

A

neurons consume oxygen, increasing the ratio of deoxygenated to oxygenated blood. This is reflected by a lag in the BOLD response (the deoxygenated phase).

39
Q

What happens in response to stimulation?

A

neural activity drives astrocytes, releasing a vasodilatory signal (e.g., nitric oxide), which causes an increase in cerebral blood inflow. As a result, the oxygen level, blood volume, and blood outflow are all increased, which is accompanied by a rise in BOLD signal (oxygenated phase) after stimulation.

40
Q

What happens in the absence of further stimulation?

A

the activity of neurons return to their resting state, accompanied by a gradual decrease in the BOLD signal (sustained response phase). (Note that an initial dip in the BOLD signal and a post-stimulus undershoot may also be obesrved

41
Q

What are the parameters of implicit casual model reduced to?

A
  1. Parameters that mediate intrinsic coupling amongst the states
  2. Parameters that mediate I stir six coupling among states
  3. Bilinear parameters that allow the inputs to modulate the coupling

Identification proceeds in a Bayesian framework

42
Q

What does the coupling parameter correspond to?

A

Effective connectivity

43
Q

What does the bilinear parameters reflect?

A

Changes in connectivity induced by inputs

44
Q

What is the basic idea of DCM?

A

Construct a reasonably realistic neuronal model of interacting cortical regions

This model is then supplemented with a forward model of how neuronal or synaptic activity is transformed into a meandered response

This enables the parameters of neuronal model (I.e. effective connectivity) to be estimated from observed data

45
Q

What does the DCM model assume?

A

The responses are driven by designed changes in inputs

46
Q

What is DCM used to test?

A

Specific hypothesis that motivated the experimental design

47
Q

What are the two ways that DCM designed inputs can produce responses ?

A
  1. Inputs can elicit changes in the state variables (I.e. neuronal activity)
  2. Changing the effective connectivity or interactions
48
Q

What are used to define neuronal and hemodynamic models?

A

Differential equations

49
Q

What is effective connectivity an influence of?

A

One neuronal system exerts over another in terms of inducing a response

50
Q

What is a response defined in terms of DCM?

A

Change in activity with time

51
Q

In the neuronal model, what are the parameters c (A, Bj,C)?

A

The connectivity or coupling matrices that we wish to identify and define the functional architecture and interactions among brain regions at a neuronal level

52
Q

What are the purpose of DCM model?

A

Models the changes from one time point to the next to make sense of how brain regions impact in each other’s neural activity

53
Q

What are purely based on the BOLD signal of distant regions?

A

Functional and effective connectivity

54
Q

What is structural connectivity measured through?

A

Tractography

Diffusion tensor imaging (DTI)

55
Q

What is BOLD signal not a direct measure of?

A

Neural activity

56
Q

What is BOLD signal?

A

Dependent measurable variable (or observed) of the underlying neural activity that cannot be measured with fMRI

57
Q

What does functional connectivity describe?

A

Statistical dependencies between regions I.e. correlations

- which voxels in the brain display similar BOLD signal fluctuations over the course of a scan

58
Q

What is effective connectivity defined by ?

A

A model and corresponds to the directed influence that one region exerts in the rate of change of activity in another

Directed influence one region has over another

59
Q

What does the measures of effective connectivity in DCM consider?

A

The rate of change of neural activity with respect to time - in response to some incoming signal

  • another brain region
  • exogenous environmental stimulus
60
Q

What can experimental manipulations change?

A

The effective connectivity strength of a connection to produce bilinear effect

61
Q

What does DCM estimate?

A

Coupling parameters given the structure of the model and observed data

62
Q

What is the purpose of DCM analysis?

A

To estimate the coupling parameters of a model and evaluate how well a particular model explains the observed data

63
Q

What does model inversion allow?

A

To compute the evidence for each model, the estimation procedure

How well it explains the data

64
Q

What can the model evidence subsequently be used to compare?

A

A series of models to assess which of a number of plausible model is the most likely to have generated the observed data

This necessitates the investigator to have a series of equally likely competing hypothesis of underlying functional architecture (or model space) to test apriori

65
Q

What can the model space take?

A

What he took of two models that do and do not possess a connection between two regions or models in which that connection is or is not modulated by an experimental manipulation

66
Q

What has Bayesian model selection (BMS) been extended to compare?

A

Models in group studies and compare different families of similar models

1) what is the underlying functional architecture of network of brain regions?
2) which connections are modulated by experimental manipulation?
3) are the coupling parameters of a network of brain regions different in two groups of people (e.g. patients vs healthy controls)

67
Q

What does DCM attempt to model?

A

Observed data at a number of regions thus it is essential that comparisons of coupling parameters only occurs between models of the same architecture

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