Synthetic Brain Imaging: A Computational Interface Between Electrophysiology and Neuroimaging

Bonaiuto, [email protected]

Synthetic Brain Imaging: A Computational Interface Between Electrophysiology and

Neuroimaging

James Bonaiuto (work with Michael Arbib, USC)

Andersen Laboratory

California Institute of Technology


The Challenge of Data Integration and Interpretation

Two of the major problems facing cognitive neuroscientists are data interpretation and comparison across modalities

Neuroimaging studies are typically designed to test some conceptual model of the interactions between the brain regions involved in a task.

The results are usually evaluated using an ad-hoc verbal analysis and compared to neurophysiological data.

?≈

Rizzolatti et al (1995) Buccino et al (2004)


From Neural Activity to BOLD Response

Neural activity Signalling Vascular response

Vascular tone (reactivity)Autoregulation

Metabolic signalling

BOLD signal

glia

arteriole

venule

B0 field

Synaptic signalling

Blood flow,oxygenationand volume

BOLD signal ≠ Neuron firing

(figure byRichard Wise)


Synthetic Brain Imaging

Synthetic Brain Imaging was developed to address the disconnect between experimental modalities

Computational models based on neurophysiological data are used to generate simulated neuroimaging signals: regional cerebral blood flow (rCBF) blood oxygen level-dependent (BOLD) responses

This technique has been used to successfully predict: A projection from the PFC to the anterior intraparietal region AIP (Arbib, Fagg & Grafton, 2002)

Populations of sound contour-selective cells in secondary auditory cortex (Husain et al., 2004)


It depends on the data the model is intended to address Early synthetic brain imaging approaches used firing rate models (Arbib et al., 1995; Horwitz et al,. 1998)

We require the simplest possible model that can produce a wide range of firing patterns

What is the Appropriate Neural Model?

(Izhikevich, 2004)


Neurophysiology and fMRI

Under some conditions spiking activity and CBF dissociate

In general, local field potential (LFP) is a better predictor of BOLD than spiking activity

LFP most directly reflects synaptic rather than spiking activity

We therefore require a model with realistic synaptic activity

(Goense & Logothetis, 2008)

(Lauritzen et al.,

2003)


We use the sum of synaptic conductances as the measure of neural activity

Several mechanisms coexist to regulate blood flow neuron-astrocyte pathway (Koehler et al., 2006) vasomotor GABAergic interneurons (Cauli et al., 2004) nitric oxide diffusion (Metea & Newman, 2006)

We use a generic blood flow-inducing signal that subsumes neurogenic and diffusive components (Friston et al., 2000) A linear function of neural activity with signal decay and autoregulatory feedback from blood flow

Neurovascular Coupling

AMPA NMDA GABAa GABAbu t g t g t g t g t

0

0

1in

b f

u t u fdb b

dt u

=gain parameteru0=baseline synaptic activityb=decay time constantfin=blood flowf=feedback time constant


Vascular Signal Generation: Balloon Model

We use Friston & Buxton’s balloon model to simulate the vascular response to neural activity and generate simulated PET or fMRI signals


A New Synthetic Brain Imaging Model

Izhikevich neurons with realistic synaptic dynamics and noise

Total synaptic conductance for all synapses in a voxel used to generate to a generic blood flow-inducing signal

Use normalized blood flow-inducing signal as input to the Balloon model


Basic Network Architecture

Pyramidal Neuron Firing Rate (Hz)

{Response latency

– Populations of pyramidal neurons and inhibitory interneurons– Center-surround connectivity implements winner-take-all dynamic– Depending on the input, there can be a considerable latency

before the network settles on a stable winner

Membrane Potential (mV)

0

100

1 100Neuron

0

2.0

Tim

e (s

)

-80

30

0

-50


Example 1: Random Dot Motion Discrimination

The random dot motion

direction discrimination task is

commonly used to study

perceptual decision-making Task: saccade in the net movement direction of a field of randomly moving dots Stimulus coherence: percentage of dots moving in the same direction

This task is useful in demonstrating the power of synthetic brain imaging because A well-defined network of brain regions is involved (MT, LIP, FEF) There exists neural recording, microstimulation, behavioral and imaging data using the task in humans and non-human primates


Model Derivation

We used three connected WTA networks to simulate the MT-LIP-FEF network

Neural parameters were set using values from experimental data

Network parameters were set using a genetic algorithm that used the model’s

fit to neural

recording and

behavioral data

as the fitness

function

(Gold & Shadlen, 2007)


Results: Neural Activity

Stimulus Coherence3.2% 12.8% 51.2%

Pyramidal neurons in LIP converge on a population code centered on the chosen saccade direction

Response time was interpreted as the time taken for max firing rate in FEF to reach 100Hz (Hanes & Schall, 1996)


Results: Behavioral Measures

Response time and accuracy were fit to the same psychometric and chronometric functions used to analyze human data

Fitted parameters were within the range of human performance

Model Performance Human Behavioral Data

(Palmer et al., 2005)


Results: Microstimulation Simulations

Simulation Results Monkey Data

Control

MT Stim

LIP Stim

MT microstimulation biased decision process and reaction time LIP microstimulation had the same effect, but to a lesser extent The same results are found in monkey microstimulation experiments

(Hanks et al., 2006)


Results: Synthetic fMRI

MTLIPFEF

Synthetic fMRI Human fMRI

(Rees, Friston & Koch 2000)

The model replicated human fMRI data that only found a positive correlation between BOLD response and stimulus coherence in MT

In the model this is because intraregional processing (WTA) dominates LIP and FEF activity and is roughly the same at each coherence level


Example 1: Summary

A basic neural microcircuit was connected in a network based on anatomical considerations

Neural parameters were set using values from experimental data. Network parameters were set using a genetic algorithm that fit the firing rate and model behavior to neural recording and psychophysical data

The model was validated by replicating microstimulation and fMRI studies


Example 2: Reach Target Selection

We developed a model of the parieto-frontal reach circuit with each region based on macaque neurophysiological data and interregional connections constrained by tract-tracing studies

The output of the dorsal premotor region was decoded and used to control a simulated arm/hand


Synthetic PET: Comparison with Experimental Data

% change in rCBF in F2, F6, V6a, and PFC matches published neuroimaging data (Savaki et al., 1997, left)

Differences in activity in V4 highlight a PFC→V4 connection overlooked in model construction

Updated model activity closely matches published data (right)

0.0

0.8

0.4

Sig

nal %

Cha

nge

F6F2 LIP V6A PFC V4 F6F2 LIP V6A PFC V4


Example 2: Summary

A virtuous cycle of models and experiments

We used available connectivity and neural recording data to develop a model of reach target selection

Synthetic brain imaging was used to compare the global model activity to metabolic signals in the monkey brain

This comparison was used to update the model to include feedback connections from PFC to V4


Peigneux et al (2004) Looked at familiar vs novel gesture imitation Found that brain areas associated with gesture recognition and production were not more active for familiar vs novel imitation

Example 3: Imaging of a Cognitive Model of Apraxia

Red: visuo-gestural codingGreen: input praxiconBlue: output praxicon


Synthetic PET on a Cognitive Model of Apraxia


Example 3: Summary

A model of gesture recognition and imitation was used to generate PET predictions that conflict with those generated in an ad-hoc manner using a conceptual model

The model is simple, but nonetheless more complex than the conceptual one used by experimentalists

Data necessary to constrain the model do not exist – the model demonstrates one possibility

In this situation multiple competing models should be developed and used to determine an experiment that could disambiguate them


Summary

Synthetic imaging can bridge the gap between electrophysiology and neuroimaging

We gave three examples using this technique to Validate a neural model Refine a neural model Offer a novel interpretation of experimental data

Future studies could use this technique to generate predictors for fMRI analysis


Arbib LabRob Schuler

Itti LabDavid BergFarhan Baluch

FundingNSFSloan Foundation

Thank you

Documents

Synthetic Brain Imaging: A Computational Interface Between Electrophysiology and Neuroimaging