Data-Driven Network Neuroscience

Sarah Feldt Muldoon Mathematics, CDSE Program, Neuroscience Program DAAD, May 18, 2016

What is Network Neuroscience?! Application of network theoretical techniques to neuroscience data in order to quantify and understand brain structure and function.

What is a Network? A network is a collection of nodes (vertices) connected by edges (links).

Adjacency Matrix

nodes  edges  

1

2

3

4 5

6

7 8

9

10 11

12

nodes  edges  

Real-world Networks: Systems to Graphs Dolphin Social NetworkNodes: Bottlenose dolphinsEdges: Social interactions

Ahn et al. (2011) Scientific Reports

Bollen et al. (2009) PLoS ONE

Lusseau (2007) Evol Ecol

“Map of Science”Nodes: Journals Edges: Click stream data

Food Flavor NetworkNodes: IngredientsEdges: Shared flavor compounds

Fun Brain Facts •  The human brain has ~1011 neurons

•  Each neurons has ~ 104 synapses •  Your brain has fired ~ 1013 action

potentials in the last minute

Types of Neuroscience Data Recent explosion in experimental techniques •  Structural data •  Time series data (functional data) •  Simultaneous recordings across

modalities • Data across multiple scales

Macro-scaleMicro-scale

Multi-electrode Array ECoG

Ca imaging fMRI

Spat

ial r

esol

utio

n

Temporal resolution

+

+-

-

How do we integrate across scales, modalities, and temporal evolution?

Networks in Neuroscience Choice of scale determines how networks are built

Micro-scale Macro-scale

Neurons Brain Regions

Defining Nodes: Choice of Scale • Micro-scale: Neurons

Excitatory or Inhibitory

• Meso-scale: ECoG sensors, probes

Anatomically defined Functionally defined

• Macro-scale: Brain Regions

Defining Edges: Anatomical Networks Structural networks: reflect the underlying anatomy

Meso/Macro-scale: white matter tracts between brain regions

Micro-scale: synapses (chemical transmission) or gap junctions (electrical)

Pereda, (2014) Nat. Rev. Neuro

Additional Information: Node Dynamics We can take advantage of the fact that we observe node dynamics (underlying structure is often more difficult to determine)

Discrete dynamics

Neuron 1:Neuron 2:Neuron 3:

Time

Continuous dynamics

Defining Edges: Functional Networks

• Discrete spike train data

• Continuous data: electrodes, fMRI, MEG, etc.

Neuron 1:Neuron 2:Neuron 3:

Derived from statistical relationships between node dynamics

Time Statistical Relationships (correlations)

Functional Connectivity

Defining Edges: Other Considerations • Choice of frequency band

•  Bandpass filter and operate in time domain •  Choose measures that operate in frequency domain:

•  Coherence, wavelets

• Weighted vs Binary Networks

•  Thresholding can potentially throw away important information

about network structure •  Comparing graphs: van Wijk et al. (2010) PLoS ONE •  Weak connections in schizophrenia: Bassett et al. (2012) NeuroImage

Telesford et al. (2013) Front Neurosci

Why Study Brain Networks?

Understanding network organization can provide insight into

brain function (dysfunction)

Compare structure between groups •  Healthy vs. pathological: Identify biomarkers

Understand individual differences •  How does structure give rise to function and performance?

Study network evolution •  How does structure change over time (learning, disease progression, etc)

Data-driven Network Neuroscience

From Data to Networks Micro-scale data

Calcium imaging

Muldoon et al. (2015) Brain Goldberg Lab, U of Penn

From Data to Networks Meso-scale data

ECoG

Lesser and Webber, Johns Hopkins

6 SD5-Ref

5 RH54-Ref

10

20

30

40

50

60

70

80 1

2

3

4

Time Window

Com

munityN

odes

(ele

ctro

des)

1 2 3 4 5 6 7

Window 1

Window 2

Window 3

Window 4

Time

Nodes: 83 brain regions(Lausanne 2008 atlas)

Network Structure

Edges: white matter tracts(diffusion spectrum imaging)

Network Dynamics Functional Connectivity

+

Computational model: coupledWilson-Cowan oscillators

0 0.5 1Correlation strength

Brain

regio

ns

A

B C

From Data to Networks Macro-scale: MRI Data

Jean Vettel, ARL Scott Grafton, UCSB Matt Cieslak, UCSB, Clint Greene, UCSB, John Medaglia, Penn

0 0.5 1

Network Structure

Dynamics

Functional Connectivity

fMRI data (task data)

From Data to Networks New Data: Tractography in rodents

Poulsen and Schweser, UB: Rat Data

Quantifying Brain Network Structure • Many of the techniques and measures used to study brain

networks were originally developed for social networks

• However, there are some important differences between the properties of the data between the two systems •  Often weighted networks •  Not necessarily sparse networks •  Nodes are dynamic

• Now many methods have been adapted to reflect the properties of neural data, but this is an active area of research that continues to be developed

Necessary skills Data analysis: extracting networks from times series or imaging data •  Signal processing •  Image analysis •  Computational abilities

Metric development: novel methods for quantification of network structure tailored to properties of neuroscience data •  Applied math (networks) •  Topology •  Computational abilities

Need for researchers with highly interdisciplinary skill sets

•  New PhD program •  MS in discipline required •  Fellowships

140,000-160,000 new jobs

in CDSE related areas

cdse.buffalo.edu

Ph.D. Program in

Computational & Data Enabled

Science (CDS) “This is a program that properly addresses the need to cover the two important areas of data and computation in an integrated manner…”

“UB is ahead of the efforts that this reviewer has seen to date.”

CDSE Program

Talk tomorrow by Abani Patra 3:05 PM

Thanks to… •  Muldoon Group

•  Michael Vaiana •  Henry Baidoo-Williams •  Minwei Ye •  Kanika Bansal

•  Many collaborators •  Dani Bassett Lab, U of Penn. •  Jean Vettel, US Army Research Laboratory •  Scott Grafton, UCSB •  Matt Cieslak, USCB •  Clint Greene, UCSB •  John Medaglia, Penn •  Ron Lesser, Johns Hopkins •  Bob Webber, Johns Hopkins •  Ethan Goldberg, Penn/CHOP •  David Poulson, UB •  Ferdinand Schweser, UB •  Kostas Slavakis, UB •  David Wack, UB •  Mark Bower, Mayo Clinic

Funding:

Questions?