Week 1 - introduction

Question 1

What is a model?

Answer 1

Simplified abstraction of data or a system

All models are wrong, but some models are useful

Question 2

how can models vary ?

Answer 2

Explanatory power - From descriptive to mechanistic

Spatial scale = Macro to micro

Biological realism - abstract to realistic`

Question 3

Why build models of the nervous system?

Answer 3

Make assumptions explicit

Make experimental predictions and generate hypothesis

Reduces dimensionality of nervous system data

Understand the structure/function/development/disease

Question 4

what are nodes and edges in networks?

Answer 4

Nodes are the building blocks of the network
Edges are the relationship between nodes

Question 5

what is the range of spatial scales that connectomics can be used for?

Answer 5

Micro - single cells
Meso - Local-circuits / processing units
Macro - Large scale grey areas

Question 6

Describe flywire

Answer 6

The first complete connectome of the entire drosophila brain

5x10^7 synapses described between 139,000 neurons

Question 7

What is the range of temporal scales that connectomics can study changes across

Answer 7

Fast - intrinsic dynamics (ms) e.g hippocampal cells in vitro, fmri dynamics

Mid - e.g experience dependent plasticity over days

Slow - Ageing and development over years

Question 8

describe connectomics in structural and functional networks

Answer 8

Connectomics can be used to study structural and functional brain networks

Structural networks shape the functional networks on a fast time scale (seconds/minutes) - this is known as brain dynamics

Function shapes structure on a slow time scale - this is known as plasticity (days/ years)

Question 9

describe network types:

Answer 9

Binary : simple, either there is a connection between two nodes or there isnt

Weighted : edges have weight meaning some connections can be stronger

Directed: edges have a direction

real brain networks are weighted and directed but human connectome models are usually weighted and undirected. This is because recovering the directionality of connections is quite difficult (it requires causal statistical models or invasive imaging)

Question 10

descirbe networks as a matrix

Answer 10

Networks are represented as square matrices

Rows and columns correspond to nodes

Entries in the matrix indicate whether theres an edge between the two nodes, and its weighting

Question 11

what is a network neuroimaging pipeline?

Answer 11

1. imaging (e.g diffusion mri, structural or functional mri)
2. Parcellation (defining nodes, grey matter is subdivided into smaller areas)
3. Connectivity (Defining edges)
4. Thresholding (only retaining some connections and setting others to zero)
5. Analysis

Question 12

What is the pipeline of defining nodes in human MRI

Answer 12

Nodes are defined using structural MRI. T1w MRI scans allow you to define white matter.

Then segmentation can be applied, which involves subdividing/ labelling different tissues (grey vs. white matter vs. csf). This uses machine learning techniques e.g clustering

Then parcellation is applied where the grey matter is divided into subregions. This can be done using anatomical principles (with anatomical atlas), or it can be done randomly. Or it can be done based on homogenous structure/function between regions.

Question 13

how can you show that your findings aren’t parcellation dependent

Answer 13

Repeat it with different parcellations

This is important because it defines the nodes, and different nodes = different connectivity

Question 14

describe DTI tractography, the structural connectome to define edges

Answer 14

It is derived from diffusion weighted imaging and tractography

diffusion imaging measures the movement of water molecules within each voxel in the brain. If the voxel has a white matter pathway the water molecules will tend to move in the direction of the white matter pathway. If not, the water molecules will tend to move at random. T

(voxel = 3d pixel)

Tractography involves analysing these directions to determine white matter tracts

Question 15

describe morphometric similarity networks to define edges

Answer 15

ANATOMICAL Measures features such as grey matter thickness, volume,, curvature etc, for each region

Then you estimate correlations between these features for pairs of regions to determine whether the regions are similar in their morphometry

This has been proven to show a strong overlap with connectivity measures, suggesting this can be an indirect way of measuring anatomical connectivity.

An advantage of this is that you can use only a T1 weighted scan to do this (you don’t need diffusion imaging as well)
A disadvantage is that it’s more indirect

Question 16

describe structural covariance networks to define edges

Answer 16

like morphometric similarity except you only measure a single feature e.g grey matter thickness, and compare it across participants .

So the question is, for each two regions, are they similarly thick across all participants

So measures does the anatomy of two pairs of regions vary across a population

This produces a structural covariance matrix

Question 17

What is a similarity networks?

Answer 17

Combining SCN, MSNs and other properties to measure similarity between regions.

Then use these to definine a similarity network - an indirect measures of connectivity between networks

Question 18

What is the functional connectome?

Answer 18

Combine nodes with functional synchrony to see if the activity between nodes is correlated over time

Commonly used with functional MRI, measuring changes in BOLD signal

This is often static, meaning it is time averaged

Question 19

what is dynamic vs static functional connectivity?

Answer 19

static methods generate a single network per scanning session

But dynamically transient ‘states’ can be derived as well. This is where you take a window of the time series and slide it along
Also you can look at co-activation patterns (CAPS) or hidden markov models. This is where you take a window of the time series and slide it along

Question 20

why is studying structural and functional connectivity important?

Answer 20

• Studying relationships between structure and function
• We can learn how structural networks constrain functional networks
• we can study how this relates to grey matter atrophy in neurodegenarative disorders. Studies show that networks with high functional and structural connectivity are more likely to show grey matter atrophy in neurodegenerative diseases

Question 21

What is structure function coupling?

Answer 21

Take each edge between a structural and functional network and correlate them across participants, to obtain a matrix of structure- function cor correlation

You can also see how this correlation varies between brain regions

Question 22

Describe thresholding to remove weak edges

Answer 22

Most of these networks are dense, meaning that a relationship is estimated between almost every possible pairs of regions

Thresholding makes sparse networks, removing some of the weak connections

Optionally, these networks can also be binarised

Question 23

Describe absoloute vs proportional thresholding with pros and cons

Answer 23

Absolute - retaining edges above a certain weight

Proportional - retaining a fixed percentage of the strongest edges, e.g retain Y% of the strongest edges. This helps remove bias based on different density of edges for different ppts

Question 24

describe different methods to study adolescent covariance of brain connectivity

Answer 24

• use a sliding window over structural covariance maps sorted by age to see how structural covariance changes over time (vasa et al, 2018)
• You can look at changes in functional connectivity between regions as someone ages