1. Comparing Animal and Human Connectomes
Garrett Winkelmaier

2. Overview Introduction to Connectome Data Human Connectome Work
Cross-species Connectome Work
Project Ideas

3. Stuctural Connectivity
describes anatomical connections linking a set of neural elements
White matter projections
Stable network over short periods (seconds to mintues)
Theory: experience-dependent over longer periods (hours to days)
Usually undirected networks as direction of projection can’t be measured

4. Structural Connectivity Visualized
From imaging structural brain connectivity to network metrics. The three plots show three different ways to represent structural connections in anatomical space.
A set of tractography streamlines. Red, green and blue indicate fibers running along the medial-lateral, anterior-posterior, and dorsal-ventral direction, respectively.
(B) A network diagram of nodes (red) and edges (blue), with edge width indicating the edge strength, calculated as the streamline density linking each node pair. For clarity, only the strongest edges are shown.
(C) A plot representing a nodal network measure, in this case the node betweenness centrality. Highly central nodes are found in medial parietal as well as cingulate and frontal cortex. Data replotted from ref 56.

5. Functional Connectivity
Time series observation that describes a statistical dependence
electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI)
cross-correlation, mutual information, or spectral coherence

6. Extraction of brain networks from brain measurements and recordings
Extraction of brain networks from brain measurements and recordings. The basic workflow follows four main steps.
Definition of network nodes, either by parcellation of the brain volume into structurally or functionally coherent regions (left), or on the basis of placement of sensors and/or recording sites (right); (2) Definition of network edges, either by estimating structural connections from structural or diffusion imaging data (left), or by processing time series data into “functional edges” that express statistical dependencies (right);
(3) Network construction, by aggregating nodes and edges into a connection matrix representing a structural (left) or functional network (right). The example plots are from previously published data56,95;
(4) Network analysis.

7. Community Detection in Functional Connectivity
Many different methodolgoies
Issues with how to threshold
Threshold – often leaves less than 10% then set to unity edge weight
this leads to extremely sparse binary network to work with
Methodological issues in the analysis of functional connectivity. Panels (B) and (C) illustrate the effect of thresholding and binarizing. Panels (D) to (G) illustrate the issue of degenerate solutions in modularity.
A whole-brain functional connectivity matrix generated by averaging over approximately 1000 participants imaged in 18 imaging centers worldwide, as part of the “1000 Functional Connectomes” dataset (F1000). Nodes are arranged according to the Harvard-Oxford Atlas (comprising 112 cortical and subcortical regions). Data are averaged and processed as described in ref 39.
The same matrix as shown in (A) after applying a threshold that retains only the top 10 % of all connections. The remaining connections have been binarized (set to unity strength; black squares).
Optimal partitioning and rearrangement of nodes according to modules. A total of five modules are found, with the majority of binary connections arranged within these modules.
The same matrix as shown in (A), after optimizing modularity but without thresholding.39 Four modules are identified, with a maximal Q =
The same matrix as in (D) but with modules indicated in a block structure.
Mutliple applications of the modularity optimization algorithm (here 1000) yielded a number of unique solutions (here 47) that are displayed in the form of the consensus matrix.41 Different gray levels refer to the number of times each node pair was placed into the same module. While the first module appears intact across nearly all solutions, the last two modules display a complex consensus structure, suggesting that they are closely linked.
Three out of the 47 unique individual solutions found, with value of Q = , Q = , and Q = (left to right). Note that the last two modules have joined in the third example, and that module 1 remains intact across all three solutions.

8. Multi-Scale Modularity
Community detection is highly sensitive
we illustrate the multi-resolution approach for “sweeping” through a range of resolution parameters to detect communities at different scales, this time using a synthetic network constructed to have hierarchical community structure (hierarchical levels that divide the network into 2, 4, and 8 communities)

9. Interconnectivity of modules
Modules, cores, and rich clubs.
(A) A schematic network composed of four modules that are linked by hub nodes (black). These hub nodes are clearly important for connecting modules to each other, but they are only weakly interconnected amongst each other.
(B) With the addition of further inter-module connections hub nodes now form a densely interconnected rich club, consisting of 5 nodes with a degree of 4 or higher.
(C) The same network as shown in (B), but now shown after core decomposition, (ie, the iterative removal of low degree nodes, shown here in gray). This procedure results in a core network comprising 4 nodes with a minimal degree of 3.

10. Sensitivity to Threshold
K is the node degree
“Feeder Edge” – Rich node to non-Rish node connection
We show edge classifications at three different values of k, in order to highlight that classifications (and the subsequent interpretation) can vary dramatically, even across statistically significant rich clubs.
Feeder edges - rich node to non-rich node

11. Structural-Functional Relationship
Relation of structural to functional connections. All data shown here are represent the right hemisphere of cerebral cortex (averaged over 5 participants), replotted from refs 56,95.
(A) Structural connectivity (SC) matrix, with edge weights resampled to a Gaussian distribution.
(B) Empirical resting-state functional connectivity (FCemp), expressed as Pearson correlations of fMRI time series (average of two runs per participant, 35 minutes total length).
(C) Simulated functional connectivity (FCsim) obtained using a neural mass model (average of 8 runs of 8 minutes simulated time).95,164
(D) Correlation between SC and FCemp (R = 0.57).
(E) Correlation between SC and FCsim (R = 0.51).
(F) Correlation between FCemp and FCsim (R = 0.46). Correlation plots show regression lines in red, and are computed over structurally connected node pairs in panels (D) and (E), and all node pairs in panel (F).

12. Connectome Summary Well defined network parameters
Many analysis using graph theory
Network analysis improves as technology of measurement devices improve
High interest area (large number of studies)

13. Human Connectome Work
Disease & Developing brain

14. Disease Studies Comparing healthy brains to diseased brains
The nodes are arranged vertically by degree and are separated horizontally for clarity of representation.
The numbers indicate approximate Brodmann area and the prime symbols (′) denote left-sided regions
The clustering coefficient of each node, a measure of its local connectivity, is indicated by its size
a | The brain anatomical network of the healthy volunteers has a hierarchical organization characterized by low clustering of high-degree nodes2
b | The equivalent network constructed from MRI data on people with schizophrenia shows loss of this hierarchical organization — high-degree nodes are more often highly clustered.

15. Developing Brain Studies
Reasoning ability test
165 typically developing children
3 tests, done 1.5 years apart
Developmental pattern of reasoning ability in 165 typically developing children and adolescents. Red, green, and blue dots indicate performance on the participant’s first, second, or third behavioral testing session, respectively. Red lines show changes in performance from the first to the second visit, and green lines show changes from the second to the third visit. The average delay between assessments was approximately 1.5 years.

16. Cross-species Connectome Work

18. Community Structure Communities can be found across multiple species
Sensory
Anatomical
Motor skills
Inter-neuron connections

19. Human v. Chimp Increased horizontal spacing distance (HS
Greater HSD shows an increased cortical expansion
Useful for integrating information

20. Human v. Chimp (cont)
Notice that humans have more highly connected hubs distributed within a lateral frontoparietal network
The parietal lobes can be divided into two functional regions. One involves sensation and perception and the other is concerned with integrating sensory input, primarily with the visual system. The first function integrates sensory information to form a single perception (cognition).

21. Cross-species summary
Humans show high node degree and betweenness centrality in the posterior cingulate
The degree distribution is also unique between species
Fairly small changes to core brain network enable higher-order relational thinking
This suggests that the posterior cingulate is particularly important for integrating information across a wide range of modalities and is also an efficient route for passing information between systems.

22. Project Ideas

23. Project Ideas Different network structures based on species
Can this lead to understanding functions of brain regions better
Do flies have larger connection between visual sensory and motor skills communities (Reflexs)
Do network structures such as Scale Free and/or Small World networks appear in different species
Does the affect the ability of the brain
Influences that one community has over another
Flight or fight response?
Open to other thoughts or suggestions

24. References
Betzel, Richard F., and Danielle S. Bassett. "Multi-scale brain networks." Neuroimage (2016).
Bullmore, Ed, and Olaf Sporns. "Complex brain networks: graph theoretical analysis of structural and functional systems." Nature Reviews Neuroscience 10.3 (2009):
Miranda-Dominguez, Oscar, et al. "Bridging the gap between the human and macaque connectome: a quantitative comparison of global interspecies structure-function relationships and network topology." Journal of Neuroscience 34.16 (2014):
Sporns, Olaf. “Structure and Function of Complex Brain Networks.” Dialogues in Clinical Neuroscience 15.3 (2013): 247–262. Print.
van den Heuvel, Martijn P., Edward T. Bullmore, and Olaf Sporns. "Comparative connectomics." Trends in cognitive sciences 20.5 (2016):
Vendetti, Michael S., and Silvia A. Bunge. "Evolutionary and developmental changes in the lateral frontoparietal network: a little goes a long way for higher- level cognition." Neuron 84.5 (2014):

25. Questions