1. Types of Brain Connectivity By Amnah Mahroo
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2. Background Localization of brain function!
Most studies on brain function build on the concept that different brain regions support different forms of information processing
Yet, no brain region exits in isolation
Neuroanatomists have mapped anatomical connections between brain regions in an attempt to understand structural connectivity of brain
Information flows between regions via action potentials carried by axons
Functional neuroimaging studies rest on the assumption that measurable behaviours are caused by neural activity, and that experimental stimuli change neural activity. This activity occurs throughout the brain, especially so in spatially distributed regions that are specialised to process the stimuli or behaviour in question. These different regions are often considered to be distinct processing modules. This concept is referred to as “functional segregation”. However, the neuronal processing is more than the sumof activity in these modules — the regions need to communicate; i.e., pass information to each other in an optimal way in order to process stimuli and execute optimal behaviour. This concept is known as “functional integration”.
But this provide a limited picture of information flow in brain
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4. Types of Brain Connectivity
Structural Connectivity
Functional Connectivity
Effective Connectivity
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5. Anatomical Connectivity
A pattern of structural connections between regions based on known axonal projections, i.e., fiber tracts
This has resulted in the mapping of major fiber tracts & have provided general wiring diagram of human brain
The whole set of such fiber tracts in the brain is called white matter
Anatomical connectivity, also called structural connectivity, which forms the connectome through synaptic contacts between neighboring neurons or fiber tracks connecting neuron pools in spatially distant brain regions. The whole set of such fiber tracks in the brain is called white matter. On short time scales (sec, min), anatomical connections are quite persistent and stable, while for longer time spans substantial plasticity may be observed.
This research has involved dissection and axon staining in brains of dead humans and non-human primates
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6. Diffusion Tensor Imaging (DTI)
DTI is a technique that detects how water travels along the white matter tracts in the brain
All neurologically normal individuals share the same fiber tracts
Information about the location and integrity of fiber tracts is collected via DTI
But, the integrity of these tracts, eg., myelination of axons does vary between individuals
DTI provides no info about brain function, only gives info about brain structure
DTI is noninvasive
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7. Functional Connectivity
A pattern of functional relationships among regions, inferred from common changes in activation over time (temporal correlation), that may reflect or indirect links between those regions
Why use fMRI for determining functional connectivity
Functional connectivity, which is defined as the temporal dependency of neuronal activation patterns of anatomically separated brain regions. It reflects statistical dependencies between distinct and distant regions of information processing neuronal populations. Hence, it is basically a statistical concept which relies on such statistical measures as correlation, covariance, spectral coherence, or phase locking. Statistical dependencies are highly time dependent and fluctuate on multiple time scales ranging form milliseconds to seconds.
Data collection from entire brain every few seconds providing complete spatial coverage at moderate temporal resolution
Condition: Right hand movement
- Spatial resolution vs spatial coverage
- Temporal resolution
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8. From coactivation to connectivity: A conceptual overview
Example Working memory task
Coactivation:
When two or more distinct regions show simultaneous activity during an experimental task
Coactivation does not imply that the regions are functionally connected
Prefrontal cortex
Parietal cortex
Fusiform gyrus
Inference drawn:
these regions are part of single functional system, not a network (because of unknown connectivity and causality)
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9. Model for functional connectivity
Some aspects of connectivity can be deduced by measuring the covariance or correaltion in activities among the regions during different experimental conditions
Condition X B A
This shows a double disassociation between regions A and B, one manipulation has an effect on A but not B, and second manipulation has effect on B but not A
C provide information about connectivity
Information from anatomical connections C
Condition Y B A C
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10. Combining fMRI with DTI
An elegant example of the power of combining DTI with fMRI data was published in 2007 by Andrews-Hanna & colleagues
Disconnection Hypothesis
Normal aging
To test this hypothesis, they collected fMRI and DTI data from two large samples, younger (18-40 yrs) & older (60-93 yrs) and evaluated
Functional connectivity between prefrontal and parietal cortices
Integrity of fiber tracts by DTI
Scores of cognitive test
This provides an example of how fMRI, DTI and behavioral data can be combined to support a simple conclusion
Degeneration of white matter
Reduce efficacy of information flow
Decline in cognitive abilities
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11. Resting-state Connectivity
Most straightforward evidence that two or more regions share functional connectivity comes from the studies of resting-state connectivity
These studies are used to identify synchronous BOLD changes in multiple brain regions while subjects lie in a MRI scanner but do not perform any experimental task
Represent intrinsic operations of brain
Reflect stimulus-independent processing
Primary tool for default network
Very trendy topic of research
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12. Effective Connectivity
Causal interactions between different regions of brain. That is, an activation in one area directly causes a modulation, activation or depression, in another area
Dynamic Causal Modeling (DCM):
A statistical approach for testing models of connectivity between brain regions based on hypothesis about how experimental manipulations alter activation and connectivity between regions
Focus is how task manipulations perturb the connections
Deals with strength and directionality of connections
Effective connectivity describes the influence one neuronal system exerts upon another, thus reflecting causal interactions between activated brain areas. It combines structural and effective connectivity into a wiring diagram which reflects directional effects within a neuronal network. Causality can be inferred from network perturbations or time series analysis (TSA). Techniques based on network perturbations generally need structural information as input, while TSA-based techniques, like Granger causality, may be considered model-free
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13. Dynamic Causal Modeling
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14. Example of DCM
Differential modulation of motor network connectivity during movements of upper and lower limbs
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