Types of Brain Connectivity

1. Types of Brain ConnectivityByAmnah Mahroo

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 Information flows between regions via action potentials carried by axons Neuroanatomists have mapped anatomical connections between brain regions in an attempt to understand structural connectivity of brain But this provide a limited picture of information flow in brain

4. Types of Brain Connectivity Structural Connectivity Functional Connectivity Effective Connectivity

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 This research has involved dissection and axon staining in brains of dead humans and non-human primates

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

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 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

8. From coactivation to connectivity: A conceptual overview ExampleWorking 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)

9. Model for functional connectivity Some aspects of connectivity can be deduced by measuring the covariance or correaltionin activities among the regions during different experimental conditions Condition X A B • 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

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

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

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

13. Dynamic Causal Modeling

14. Example of DCM Differential modulation of motor network connectivity during movements of upper and lower limbs