Interrogating structure and function
using whole-brain reconstruction

Daniel Fürth
Meletis Lab

Half-time seminar
7th March 2016

daniel.furth@ki.se

Tracing the network

The Measurement Problem

• Representational theorem
• Uniqueness theorem

The Measurement Problem

Winter & McClelland (1978)

The Measurement Problem

Winter & McClelland (1978)

The Measurement Problem

Levels of Measurement

Stevens (1946)

Labeling and Counting

Church (2015)

• Cell types

• Connections

• Connections strengths and types

• Developmental lineages

• Histories of electrical activity patterns over time 

• Histories of molecular changes over time 

Tracing the network

Pollak et al. 2014

www.mcstan.org

Whole-Brain Reconstruction

Pollak Dorocic et al. 2014

Reconstructing brain from sectioned tissue

Tracing the network

DRD2 film
56,710 neurons

Tracing the network

161,294 neurons film time

Tracing the network

Tracing the network

Tracing the network

'Google maps' of neuroanatomy

'Google maps' of neuroanatomy

Whole-brain behavioral c-Fos mapping

Basic idea:

A

B

C

Independent
variable

Dependent
variable

Mediator
variable

Mediational statistical analysis

Whole-brain behavioral c-Fos mapping

Basic idea.

A

B

C

cocaine

dosage
(mg/ml)

Behavior
total track length (cm)

c-fos expression

(ith region)

direct effect

indirect effect

mediating effect

Question: How much of the behavioral variability is explained by variability in c-fos expression?

• Regions of particular Interest
  • NAc
  • PFC
  • Dorsal striatum
  • Ventral pallidum
  • Amygdala
  • VTA
  • LH

Kourrich, Calu &  Bonci (2015)

Whole-brain behavioral c-Fos mapping

Whole-brain behavioral c-Fos mapping

Multiresolution decomposition

Multiresolution decomposition

Multiresolution decomposition

Multiresolution decomposition

scRNA-seq

NATURE BIOTECHNOLOGY | COMPUTATIONAL BIOLOGY | ANALYSIS

High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin

Kaia Achim, Jean-Baptiste Pettit, Luis R Saraiva, Daria Gavriouchkina, Tomas Larsson, Detlev Arendt & John C Marioni

scRNA-seq

Allen Brain Reference Atlas

• Atlas 2007 (manually drawn Nissl):
  • 200 μm thick coronal sections.
• Atlas 2011:
  • 100 μm both coronal and sagital
• Atlas 2014 (connectivity avrg template)
• Atlas 2015 (beginning of june):
  • 10 x 50 μm

• Registration atlas:
  • 25 x 25 μm
• Grid expression ISH:
  • 200 x 200 μm MetaIOimage (.raw, .mhd)

scRNA-seq

Allen Brain Reference Atlas

scRNA-seq

Anatomic Gene Expression Atlas

Lydia Ng,  et al. (2009) Nat. Neuro.

http://mouse.brain-map.org/agea

scRNA-seq

324 cells from cortico-striatal section

scRNA-seq

Our approach

scRNA-seq

Our approach

scRNA-seq

Allen Brain Reference Atlas

• Atlas 2007 (manually drawn Nissl):
  • 200 μm thick coronal sections.
• Atlas 2011:
  • 100 μm both coronal and sagital
• Atlas 2014 (connectivity avrg template)
• Atlas 2015 (beginning of june):
  • 10 x 50 μm

• Registration atlas:
  • 25 x 25 μm
• Grid expression ISH:
  • 200 x 200 μm MetaIOimage (.raw, .mhd)

Connectivity average template (Ng et al. 2014)

scRNA-seq

Allen Brain Reference Atlas

• Atlas 2007 (manually drawn Nissl):
  • 200 μm thick coronal sections.
• Atlas 2011:
  • 100 μm both coronal and sagital
• Atlas 2014 (connectivity avrg template)
• Atlas 2015 (beginning of june):
  • 10 x 50 μm

• Registration atlas:
  • 25 x 25 μm
• Grid expression ISH:
  • 200 x 200 μm MetaIOimage (.raw, .mhd)

Connectivity average template (Ng et al. 2014)

Do we really have a 'BigData' problem in neuroscience?

http://www.parallac.org/

10 computers (146 processors)

Up to 64 cores per processor!

Freeman et al. (2014) Nature Methods

R package

• Why R?
  • Standard data analysis:
    • load some data
    • estimate the density distribution.
    • plot it

xx <- faithful$eruptions
fit <- density(xx)
plot(fit)

R package

• Why R?

#Line 1: loading
xx <- faithful$eruptions
#Line 2: estimate density
fit1 <- density(xx)
#Line 2: draw 10'000 bootstraps
fit2 <- replicate(10000, {
    x <- sample(xx,replace=TRUE);
    density(x, from=min(fit1$x), to=max(fit1$x))$y
})
#Line 3: compute 95% error "bars"
fit3 <- apply(fit2, 1, quantile,c(0.025,0.975))
#Line 4: plot the estimate
plot(fit1, ylim=range(fit3))
#Line 5: add estimation error as shaded region
polygon(c(fit1$x,rev(fit1$x)), c(fit3[1,], rev(fit3[2,])), col=’grey’, border=F)
#Line 6: add the line again since the polygon overshadows it.
lines(fit1)

What other language can do this in 6 lines of code?

Parallel computing

• Parallel computing is extremely simple to implement from R. 

# install.packages('foreach'); install.packages('doSNOW')
library(foreach)
library(doSNOW)
cl <- makeCluster(2, type = "SOCK")
registerDoSNOW(cl)

getDoParName()


#matrix operators
x <- foreach(i=1:8, .combine='rbind', .packages='wholebrain' ) %:%
   foreach(j=1:2, .combine='c', .packages='wholebrain' ) %dopar% {
     l <- runif(1, i, 100)
     i + j + l  
   }

Concurrency and parallel programming

• Multi threaded applications through             .

#include <string>
#include <iostream>
#include <thread>

using namespace std;

//The functions we want to make the thread run.
void task1(string msg)
{
    cout << "task1 says: " << msg;
}

void task2(string msg)
{
    cout << "task1 says: " << msg;
}

//Main loop.
int main()
{
    thread t1(task1, "Task 1 executed");
    thread t2(task2, "Task 1 executed");
    t1.join();
    t2.join();
}

Rcpp

Concurrency and parallel programming

• Multi-threaded applications through             .

#include <string>
#include <iostream>
#include <thread>

using namespace std;

//The functions we want to make the thread run.
void task1(string msg)
{
    cout << "task1 says: " << msg;
}

void task2(string msg)
{
    cout << "task1 says: " << msg;
}

//Main loop.
int main()
{
    thread t1(task1, "Task 1 executed");
    thread t2(task2, "Task 1 executed");
    //let main wait for t1 and t2 to finish.
    t1.join();
    t2.join();
}

Rcpp

Dual core

Thank you!

scRNA-seq

Gene specificity

about ~24'000 genes expressed in the brain.