CARMEN: Code Analysis, Repository and Modelling for

e-Neuroscience

Research Challenge

Understanding the brain may be the greatest

informatics challenge of the 21st century

Worldwide >100,000 neuroscientists(~ 5,000 in UK) are generating vast amounts of data

Principal experimental data formats:

molecular (genomic/proteomic)

neurophysiological (time-series electrical measures of activity)

anatomical (spatial)

behavioural

Neuroinformatics concerns how these data are handled and integrated, including the application of computational modelling

In recent years new technological opportunities for data sharing have emerged with faster networks, improved database technologies, and affordable massive data storage capabilities

Neuroinformatics is increasingly exploiting these opportunities to enable data sharing, re-use of data and novel analysis based on new combinations of data that can be performed via database systems

Neuroinformatics

Need for Cooperation

Understanding the brain may be the greatest

informatics challenge of the 21st century

OECD identified a need to work cooperatively in order to achieve major advances and have established the International Neuroinformatics Coordinating Facility

Cooperation will permit:

development of common processes

best value from data – long term curation

‘mega-analysis’ of large data sets

integration of data sets across different scales and different approaches

interdisciplinary research

Technical

Multiple proprietary data formats Need for detailed, standardised and evolvable metadata Volume of the data to be analysed

Cultural

Multiple communities each acting independently Concerns about the consequences of sharing data Difficulty in appreciating how the science could be moved forwards by e-Science

Potential Barriers to Cooperation

CARMEN – Focus on Neural Activity

resolving the ‘neural code’ from the timing of action potential activity

Understanding the brain may be the greatest

informatics challenge of the 21st century

neurone 1

neurone 2

neurone 3

raw voltage signal data is collected using single or multi-electrode array recording novel optical recording, particularly the activity dynamics of large networks

Much current knowledge about brain function is based on analysis of firing patterns of individual neurones.

New computer-based data acquisition systems and techniques for recording simultaneously from many neurones means data are amassing rapidly.

Neural modelling generates massive simulated data sets that need to be processed, analysed and compared with experimental data.

Neuronal recordings can be intra- or extra-cellular recordings of single spikes, ensembles of neurones, or field potentials. All of these data are types of time-series data which require a specialised information handling system.

Electrophysiological Data

To demonstrate and sustain advances in neuroscience enabled by e-Science technology

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

To develop an extensible, client-defined ‘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 Objectives

Project Exemplar

Recording from brain tissue removed from epileptic patients (scarce tissue and

data rates up to 20 GB/h)

On line analysis by distributed collaborators will enable experiment to be defined during data collection

Repository will enable integration of rare case types from different laboratories

New knowledge will lead to advances in treatment

CARMEN Consortium

Newcastle: Colin Ingram Paul Watson Stuart Baker Marcus Kaiser Phil Lord Evelyne Sernagor Tom Smulders Miles Whittington

York: Jim Austin Tom Jackson

Stirling: Leslie Smith Plymouth: Roman Borisyuk

Cambridge: Stephen Eglen

Warwick: Jianfeng Feng

Sheffield: Kevin Gurney Paul Overton

Manchester: Stefano Panzeri

Leicester: Rodrigio Quian Quiroga

Imperial: Simon Schultz

St. Andrews: Anne Smith

CARMEN Consortium

Commercial Partners

- applications in the pharmaceutical sector

- interfacing of data acquisition software

- application of database infrastructure

- commercialisation of analysis tools

Work Packages

Data Storage& Analysis

WP1 Spike Detection& Sorting

WP2 Information TheoreticAnalysis of Derived Signals

WP 3 Data-Driven ParameterDetermination in Conductance-

Based Models

WP5 Measurement and Visualisationof Spike Synchronisation

WP6 Multilevel Analysis andModelling in Networks

WP4 Intelligent Database Querying

Hub and Spoke Project

Hub: A “CAIRN” repository for the storage and analysis of neuroscience data

Spokes: A set of neuroscience projects that will produce data and analysis services for the hub, and use it to address key neuroscience questions

CARMEN Structure

Managing vast amounts of data> 50TB primary data

Extracting value from the datadiscovery & interpretationanalysis – harnessing compute resourcescuration of services as well as data

Controlling access to the data & services

e-Science Challenges

Data

Metadata

Compute Cluster on which Services are Dynamically

Deployed

WebPortal

..............

WebPortal

Rich Clients

Sec

urity

Workflow Enactment

Engine

RegistryServiceRepos-

itory

CARMEN Active Information Repository Node

OMII:Grimoire

DAME:Signal Data Explorer

OMII/ myGrid:Taverna/ BPEL

OGSA-DAI& SRB

Gold:Role & Task based Security

myGrid & Gold:Feta, Provenance

DynasoarWhite Rose GridNewcastle Grid

• Data Collection from Electrode Array• Spike Detection

• with User Defined Threshold

• Spike Sorting• Analysis• Visualisation

Currently, this is a semi-manual process

We have an initial prototypefor automating this….

A Typical Scenario we want to Support

Signal Data Explorer

Example Workflow

SRB FileSystem

RDBMS

External

Client Spike Sorting

Service

Reporting

Dynamically Deployed Services in Dynasoar

BPEL / TAVERNA

Registry

INPUT Data

OUTPUT Metadata

Available Services

RepositoryS

ecur

ityWorkflow Engine

Query

Example Workflow Enactment

Example Graph Output

Example Movie Output

Extensible, standardised metadata for neurosciencedata formats (timing, data channels, etc.)experimental design (e.g. stimuli or drug

treatments)concurrent data (e.g. behaviour, physiological

measures) experimental idiosyncrasies (e.g. artifacts)experimental conditions (animals,

temperature, treatments etc.)

Some Remaining Challenges

Locating patterns in time-series data across multiple levels of abstraction

Reproducible e-Sciencecurating services as well as datapublic repositories of deployable servicesdynamic service deployment

Real-time expert collaboration

Some Remaining Challenges (cont.)

CARMEN

CARMEN is delivering an e-Science infrastructure that can be applied across a range of diverse and challenging

applications (not only neuroscience)

CARMEN enables cooperation and interdisciplinary working in ways currently not possible

CARMEN will deliver new results in neuroscience, computer science and medicine