Connectomic analysis

Introduction
Connectomic analysis aims to understand on how brain connection inform functionality or brain diseases. It view brain connections as a network and device network analysis approach to characterize its property.

Connectivity matrix, connectogram, graph analysis


source: 
Bernhardt, Boris C., SeokJun Hong, Andrea Bernasconi, and Neda Bernasconi. "Imaging structural and functional brain networks in temporal lobe epilepsy." (2013).


A connectivity can be derived by parcellating brain regions as "node" and tracking inter-regional connections as "edge". This creates a undirected graph of the brain network. 

The graph can be binary or weighted. A binary graph only concerns whether two regions are connected or not, whereas a weighted graph further considers connection strength.

A connectome generated from diffusion MRI fiber tracking is often called "structural" connectome.


Graph theoretical analysis quantifies the topology of a network as network measures, which allows for detecting aspects of functional integration and segregation, quantifying importance of individual brain regions, characterizing patterns of local anatomical circuitry, and testing resilience of networks to insult. To use graph analysis, one needs to (1) define a brain parcellation, (2) generates around 1 millions pathways, (3) compute the connectivity matrix, (4) compute network measures.

Network measures can be categorized into global and local measures. Global measures quantifies property related to the entire graph, whereas local measures quantifies property of a node or an edge. Sometime, the mean of all nodal measures can be used as a global measure.

source: https://sites.google.com/site/bctnet/measures Brain Connectivity Toolbox (BCT)

Each dMRI scan gives a global network measure that can be compared between groups or correlated with any variables.


Pros:
1. Study the brain structure as a graph.  
2. There are several readily available graph analysis approaches that can be used.

Cons:
1. Connectivity matrix is sensitive to tracking parameter and limited by the accuracy of the tracking algorithm.
2. How to partition the brain regions is a challenge

Questions
1. What is the biological basis for graph analysis?
2. Will network measures be affected by the choice of brain parcellation?

Reference
[1] Rubinov, Mikail, and Olaf Sporns. "Complex network measures of brain connectivity: uses and interpretations." Neuroimage 52, no. 3 (2010): 1059-1069.
[2] Bullmore, Ed, and Olaf Sporns. "Complex brain networks: graph theoretical analysis of structural and functional systems." Nature Reviews Neuroscience10.3 (2009): 186-198.
[3] Sarwar, T., Ramamohanarao, K., & Zalesky, A. (2018). Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?. Magnetic resonance in medicine.


Local connectome fingerprint




"local connectome fingerprinting" (LCF) uses on q-space diffeomorphic reconstruction to calculate the density of diffusing water along major fiber bundles from diffusion MRI. 

LCF provides a vector measurement characterizing white matter structures at the voxel level for each dMRI scan. 

LCF was found to be highly unique for each individual, making it a tangible metric of connectomic differences. 

The variability in LCF was also found to be partially determined by genetic factors, but largely plastic across time. 

The density sampled exhibits high individuality that allows us reliably identify individuals or quantify similarity between two connectome architectures.


Local connectome phenotypes predict social, health, and cognitive factors

Powell, Michael A., Javier O. Garcia, Fang-Cheng Yeh, Jean M. Vettel, and Timothy Verstynen. "Local connectome phenotypes predict social, health, and cognitive factors." Network neuroscience 0 (2017): 1-20.


Pros 

1 LCF is highly specific to individuals
2 LCF may reveal neuroplasticity over time

Cons
1. High individuality brings high group variance
2. Analyzing LCF can be challenging

Questions
1. What is the biological basis for LCF?

Reference
[1] Yeh, Fang-Cheng, Jean M. Vettel, Aarti Singh, Barnabas Poczos, Scott T. Grafton, Kirk I. Erickson, Wen-Yih I. Tseng, and Timothy D. Verstynen. "Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints." PLoS computational biology 12, no. 11 (2016): e1005203.
[2] Powell, Michael A., Javier O. Garcia, Fang-Cheng Yeh, Jean M. Vettel, and Timothy Verstynen. "Local connectome phenotypes predict social, health, and cognitive factors." Network neuroscience 0 (2017): 1-20.

Exercises:

1. Open the HCP1021.1mm.fib.gz file included in the DSI Studio package (or from https://pitt.box.com/v/HCP1021-1mm). Follow the instructions in Track-specific analysis and network measures to get a connectivity matrix, network measures, and connectogram.


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