EScience Meeting, Edinburgh, November 2006. Slide 1 CARMEN Code Analysis, Repository and Modelling for e-Neuroscience Jim Austin, Colin Ingram, Leslie.

Slide 1eScience Meeting, Edinburgh, November 2006.

CARMEN

Code Analysis, Repository and Modelling for e-Neuroscience

Jim Austin , Colin Ingram, Leslie Smith, Paul Watson, Stuart Baker, Roman Borisyuk, Stephen Eglen, Jianfeng Feng, Kevin Gurney, Tom Jackson, Marcus Kaiser, Phillip Lord, Stefano Panzeri, Rodrigo Quian Quiroga, Simon

Schultz, Evelyne Sernagor, V. Anne Smith, Tom Smulders, Miles Whittington.


CARMEN is a new e-Science Pilot Project, (UK research council funded) in Neuroinformatics.

Objectives:• To create a grid-enabled, real time ‘virtual laboratory’ environment for neurophysiological data

• To develop an extensible ‘toolkit’ for data extraction, analysis and modelling

• To provide a repository for archiving, sharing, integration and discovery of data

To achieve wide community and commercial engagement in developing and using CARMEN

CARMEN is a 4 year project: if it is to last longer, it must become financially self-sufficient.

See http://www.carmen.org.uk

The CARMEN Project

neurone 1

neurone 2

neurone 3


CARMEN Consortium

Leadership & Infrastructure

Colin Ingram

Paul Watson

Leslie Smith Jim Austin


CARMEN Consortium

Work Packages

University ofSt Andrews

TheUniversity OfSheffield


Background: What is Neuroinformatics?

Informatics applied to Neuroscience (of all sorts)

Experimental Neuroscience:Data recording, data analysis have used computers for a long time.But a great deal more can be achieved by pooling data and analysis services

Cognitive and Computational NeuroscienceModelling,

Matching models to more experimental dataMatching models to known appropriate behaviourDefining and running more sophisticated modelsRunning models in real time

Clinical NeuroscienceData-based understanding of neuropathologyNeuropharmaceutical assays and assessment


What is Neuroinformatics bringing to Experimental Neuroscience?

Getting leverage from e-Science capabilities to allow better use of data.

Example: Dataset re-use:Experimenter does experiment, records data, analyses data, writes the paper, perhaps makes the data available to a small number of colleagues.

…and then?

The dataset languishes,first on a spinning disk, then later on some DVD’s, then later still, is lost to view, as the experimenter changes

lab,…

Yet the data could be of use to other researchers…


What are the basic problems holding back dataset re-use? (1)

Two major technical problems: Data format, and Metadata

Data Format

There are different systems for Neuroscience data collection.

The data format is a particular structureThe structure may be

Proprietary: defined by a particular piece of software, and not made public

Locally generated: defined by a locally written piece of software, but not necessarily well documented

Public, but no suitable converter exists for the intending user


What are the basic problems holding back dataset re-use? (2)

Metadata problemsThe data itself is useless unless the re-user knows exactly what the data represents.(Presumably the experimenter knew)

But did they record this information in an accessible way?

Metadata is data about the datasetHow was it generated?

What were the experimental conditions? What was the culture, or what preparation, or what animal,…? What was the temperature of the recording? Etc. etc.

If the data is to be readily re-used these metadata problems need to be solved in a directly usable way

Simply describing the protocol in English is not enoughCan’t automate reading J. Neurosci yet!

There needs to be an automatically processable way of describing the experimental protocol.

Particularly true is datasets are used for a large-scale survey of data

e.g. for data-mining.


Enabling Neuroinformatics based collaboration

Solving data format problems

Force users to adopt a common format?Alienates users: they won’t do it unless they can see real benefits

Support documented formatsAdopt a common internal format, providing translators to & from this formatRely on proprietary format owners to come aboard because of customer pressure


Enabling Neuroinformatics based collaboration: solving metadata problems

Difficult problem: there are a number of attempts at solving it:

BrainML: (Cornell) brainml.org“BrainML is a developing initiative to provide a standard XML metaformat for exchanging neuroscience data. It focuses on layered definitions built over a common core in order to support community-driven extension.”

NeuroML: http://www.neuroml.org/“NeuroML is an XML-based description language for defining and exchanging neuronal cell, network and modeling data including reconstructions of cell anatomy, membrane physiology, electrophysiological data, network connectivity, and model specification”

Relevant not only for Neuroinformatics and experimental neuroscience: Part of a cross-cutting problem for all aspects of neuroscience.


As well as metadata systems for neuronal systems, there are related metadata systems which can be used by BrainML and NeuroML,

ChannelML: for defining ion channel modelsMorphML: for defining the morphology of a neuronSBML: Systems Biology markup language: models of biochemical reaction networksCellML:to store and exchange computer-based mathematical models

SBML is particularly well advanced: see http://sbml.org/index.psp.

MathML: for describing mathematical notation and capturing both its structure and content. See http://www.w3.org/Math/

Metadata is a big but soluble problem.

It is a multi-level problem, but the systems above provide a multi-level solution.

Solving Metadata problems continued:


Enabling Neuroinformatics based collaboration: Sociological problems

There is a reluctance to permit re-use amongst some experimental neuroscientists.

What do experimental neuroscientists get from allowing others to reuse their data?

If the answer is only better science, then some experimental neuroscientists will not come on board.They need to be convinced sharing that their hard-earned datasets will be of benefit to them

Names on papers?The ability to be involved in the further research?At the very least, some credit!

Some neuroscientists fear that their data will be used without their knowledge

There is therefore some reticence amongst the experimental neuroscience community.


Solving sociological problems

There are technical aspects to solutions:

Security aspects on the holding of data:Ensure that datasets can be secured: for example that they can only be re-used with the experimenter’s permission.

Security is critically important for holding of data which is still being analysed prior to publication.

…and non-technical aspects too

Bringing experimental neuroscientists on board

Ensuring that the Neuroinformatics community is properly cross-disciplinary, with good representation from the experimentalists.

Getting journals on-side

Many journals are demanding that raw/processed data be made available in order to check results.


Neuroinformatics and clinical neuroscience

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Clinical Neuroscience is about treatment of

Mental illnessBrain diseasesTrauma

Neuroinformatics has major application here, ranging from 3d imaging technologies to EEG recordings: much broader than the focus of CARMEN.

CARMEN is primarily concerned with neural recordings.

These can provide data on neurochemical effects on neural function.Overall brain states (mental illness, disease) are believed to originate in the neurochemistry (research in depression and schizophrenia suggests this).


Neuropharmaceutical assays

Neural cell culturesDifferent types:

Slice preparationsCultures grown from neural

cell linesCultures from neonate neurons

…have recordings made from them with and without added neuropharmaceuticals.

Interest is on changes in behaviour in these preparations.

Requires instrumentation and analysis techniques

Sharing these results can lead to major advances

Pharmaceutical companies are interestedSecurity implications


Work Packages

WP 0Data Storage& Analysis

WP1 Spike Detection& Sorting

WP2 Information TheoreticAnalysis of Derived Signals

WP 3 Data-Driven ParameterDetermination in Conductance-

Based Models

WP4 Measurement and Visualisationof Spike Synchronisation

WP5 Multilevel Analysis andModelling in Networks

WP4 Intelligent Database Querying


CARMEN Objectives

• Create ‘virtual laboratory’ for neurophysiological data• Provide repository for:

• archiving, sharing, integration and discovery of data• services that operate on the data

• Develop extensible ‘toolkit’ for data extraction, analysis and modelling

• Achieve wide community and commercial engagement in CARMEN • CARMEN must become financially self-sufficient after

4 years


• Bowker’s “Standard Scientific Model”1

1. Collect data

2. Publish papers

3. Gradually loose the original data

1The New Knowledge Economy and Science and Technology Policy, G.C. Bowker, E1-30-03-05

• Problems:• papers often draw conclusions from unpublished data• inability to replicate experiments• data cannot be re-used

Data in Science


• Bowker’s Model• Collect data• Publish papers• Gradually loose the original data

• Problems:• papers often draw conclusions

from unpublished data

• inability to replicate experiments• data cannot be re-used

• Solution• Data Repositories

• Computational Science• Write codes• Publish papers• Gradually loose the codes

• Problems:• papers often draw conclusions

from the results of unpublished codes

• inability to replicate experiments• codes cannot be re-used

• Solution• Service Repositories

but… codes can be lost too


CARMEN Active Information Repository

Data

Metadata

Compute Cluster on which Services are Dynamically

Deployed

External Client.......

.......

External Client

Security

Workflow Enactment

Engine

RegistryServiceRepos -

itory


C WSP

req

res

1

Host Provider

node 1s2, s5

…

node 2

node ns2

Web Service Provider

3

2: service fetch &deploy

SR

Service Repository

Dynamic Service Deployment - Dynasoar

R


DAME developed a tool to analyse large volumes of distributed signal data

CARMEN will extend this to: allow search and management of labelled data link the search results to data descriptions to allow better ranking and data analysis

Data Exploration


Metadata• Import tools enable users to describe

experimental conditions• Analysis services describe their own functionality• Registry of data and services

• “is there any data captured under conditions x, y & z?”• “what services are available to process this spike train

data?”

• Automatic provenance generation


e-Science Stretch

• Tool for locating patterns in time-series data across multiple levels of abstraction

• Dynamic service provisioning over a grid• Extensible, standardised metadata for

neuroscience• Fine-grained access control• Integrating data from multiple repositories


Work Packages

WP 0Data Storage

& Analysis

WP1 Spike Detection& Sorting

WP2 Information TheoreticAnalysis of Derived Signals

WP 3 Data-Driven ParameterDetermination in

Conductance-Based Models

WP4 Measurement and Visualisationof Spike Synchronisation

WP5 Multilevel Analysis andModelling in Networks

WP4 Intelligent Database Querying


Spike Detection & Sorting (WP1: Stirling & Leicester)

Analogue recording (digitised)

Cluster 1 Cluster 2

Doesn’t fit!

(Clustering using wave_clus)


CARMEN and spike detection and sorting

Idea is to provide many services

Several different types of spike detection algorithmsSeveral different types of spike sorting techniques

(including different types of data reduction, as well as different types of clustering)

Allow the user to test with a variety of techniques, and then choose the techniques they prefer

High speed links should allow immediate transfer of some datasets to Grid based systems

Allow experimentalist to choose near-real-time detection and sorting for immediate feedback

To assist during the experimentSlower (and more effective) techniques for later analysis off-line.

Allow comparison of different techniques on a wide variety of dataWhich is best, and for what?


Information Theoretic Analysis of Electrically- and Optically-Derived Signals (WP2: Imperial College, Manchester, and UCL)

Action potentials will be detected both electrically and optically.

Action potentials (spikes) are the primary electrical communication mechanism between neurons.

How can one interpret neuronal action potentials?

Information Theory, can be used to establish the ‘neuronal code’

quantifying how much information is carried by different potential neuronal coding mechanisms.

Issues:

Sampling problems, Spike correlation, Multimodal recording

By using Grid technology, we can assemble large quantities of optical and multi-electrode recordings and apply existing and novel techniques to its analysis.

We can make these techniques available as services.


Data-Driven Parameter Determination in Conductance-Based Models (WP3: Sheffield: Gurney et al)

Neuron modelling uses conductance based models.

[Many ionic species cross the neural membrane.Ion channels embedded in the membrane accomplish this transportThere are many different ion channel typesSetting the parameters for each type would enable better understanding of neuron operation]

Determining parameters for neural models is difficult and requires a great deal of data.

The parameters are not constant, and vary with (e.g.)Cell typePresence and concentration of neuromodulatorsTemperature

CARMEN aims to provide this volume of data, and hence to enable many of these parameters to be determined.


‘Compartmental’ modelling of morphology

Real neural morphology

approximation

(Passive) electrical equivalent


Fitting the model to current clamp data

100 ms

50 mV

100ms

50mV

Data (Wilson)

Model

Wood et al., Neurocomputing, 2003


Measurement and Visualisation of Spike Synchronisation (WP5: Newcastle, Plymouth)

More advanced analytical techniques are required to handle large scale, simultaneous recordings arising from MEAs.

Visualisation is critical to understanding what is happening

WP5 aims to: •develop reliable and robust analysis techniques to address these issues, particularly sweeping statistical methods to test if measures show significant changes•develop novel visualisation methods for displaying the results from these techniques, particularly those working in high-dimensional space•conduct real-time analysis of spike coding through a distributed Grid-enabled virtual laboratory.

Advanced (but fast) visualisation techniques are important to the whole community using CARMEN


Gravitational Clustering

Particle aggregation in gravitational clustering, (Gerstein and Lindsey(2006)). Each particle represents a cell; charges on each cells are incremented with each spike, and a force occurs between particles dependent on the charges.


Multilevel Analysis and Modelling in Networks (WP6: Newcastle, St. Andrews, Cambridge)

This WP aims to integrate the work of WP1-4, using the technology of WP0.

Understanding activity dynamics within neuronal networks is a major challenge in neuroscience

requires simultaneous recording from large numbers of neurons.

This WP will provide•integration of existing and novel network analysis techniques into CARMEN in order to build comprehensive models of network dynamics•data of exceptional quality and detailed provenance for the CARMEN repository for analysis of network properties•development of new dynamic Bayesian network algorithms to trace paths of neural information flow in networks.

For example: waves of activity in early turtle retina, recorded using Ca++ sensitive dye. (Thanks to Evelyne Sernagor, ION, Newcastle University)


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Concluding remarks

CARMEN is a recent project (funding started October 2006).The baseline support technology is still being assembled.

It’s not the first attempt at making neurophysiological recordings re-usable

But:

CARMEN will contain more than recordingsServices, workflows, capability of using multiple data formats

CARMEN builds on earlier e_Science projectsRe-use not re-invention

We have experimental neuroscientists, informaticians, and computational neuroscientists all on board

Tackling the broad range of issues from multiple perspectives.

EScience Meeting, Edinburgh, November 2006. Slide 1 CARMEN Code Analysis, Repository and Modelling for e-Neuroscience Jim Austin, Colin Ingram, Leslie.

Documents

data analysis

data recording

neurophysiological data

modeling data

data available

discovery of data

pooling data

records data