team 3: neuroinformatics

Neuronformatics and Emerging Technologies

February 19, 2007

Team 3 - Tensa ZangetsuChiranjeev BordoloiKoch GeevargheseRomerl ElizesYonesy Nunez

Agenda

Introduction Definitions Background Current Work and Experiments Current Support Links References

Introduction

Understanding the human nervous system is one of the greatest challenges of 21st century science

The topic we will focus on is neuroinformatics The goal for this presentation is a general overview

of neuroinformatics Brain informatics is a subset of neuroinformatics, but

most of the literature in neuroinformatics focuses on the brain

Definitions

Neuroscience Field devoted to the scientific study of the

nervous system Disciplines include: structure, function,

development, genetics, biochemistry, pharmacology, and pathology.

Focuses on the investigation of the brain and mind.

Definitions

Neuroinformatics intersection of neuroscience and information science. many points of contact between the neuroscience-related life-sciences

and the information sciences and related disciplines:– Life sciences: neuroscience, neurology, psychology, linguistics, biology,

chemistry, physics, etc.– Information sciences: computer science, mathematics, statistics, physics,

electrical engineering, robotics, etc. Goals of neuroinformatics:

– developing and applying computational methods to the study of brain and behavior

– applying advanced IT methods to deal with the huge quantity and great complexity of neuroscientific data

– exploiting our insights into the principles underlying brain function to develop new IT technologies.

Background

Neuroscience In Egyptian times, the heart, not the brain, was classified as the

seat of intelligence. Hippocrates was the first to indicate that the brain was the seat

of intelligence. Roman physician, Galen, further backed this by providing

evidence that Roman gladiators lost their mental faculties when they sustained severe damage to their brains.

Further studies of the brain was stagnant until the invention of the microscope. The work at first focused on the individual neurons.

Background

Neuroscience Camillo Golgi in the 1890’s silver chromate salt to

reveal intricate structures of single neurons Santiago Ramon y Cajal used Golgi’s information to

develop the neuron doctrine. The hypothesis is that the functional unit of the brain is the neuron.

Santiago Ramon y Cajal and Camillo Golgi received the Nobel Prize in 1906 for Physiology for their work on the structure of the nervous system

Background

Neuroinformatics Neuroinformatics is formally established by the

National Institute of Mental Health in 1993 under the Human Brain Project.

In the Bioinformatics realm, the Institute for Genomic Research was established in 1992 in Rockville, Maryland.

The exponential growth of information technologies especially the Internet in the 1990’s has prompted the growth of neuroinformatics.

Background

Neuroinformatics By 2000, 40 web-based projects with digital databases were

steered by the Human Brain Project This work impacts molecular biology and cellular physiology Society of Neuroscience is formally established in 2003 to

prompt the development and popularization of neuroscience to the world community.

In 2004, Program in International Neuroinformatics was established by 16 countries and the EU commission to promote international collaboration, dialogue, and support mechanisms for neuroscience application research.

Current Work and Experiments

This section will focus on:– Posit Science– Brain-Gene Ontology Project– Human Brain Mapping– Brain Computer Interface– Snapshot of published papers in Neuroinformatics


Posit Science Dr. Michael Merzenich and Dr. Henry

Mahncke with other scientists developed a hypothesis designed to rejuvenate the brain’s plasticity.

Posit Science Inc. was founded in 2003 in San Francisco to develop a software program that could test and validate these neuroscientists’ hypothesis.


Posit Science The connections in the brain are plastic, meaning that

when we learn something, the properties of our synapses and other neural circuits change, thus improving their processing speed and the fidelity of the information encoded.

As we age, this natural learning process starts to deteriorate. This slowing is at the root of some age-related memory loss.

Recent research has shown that reading the newspaper or doing crosswords can help keep older people mentally fit.


Posit Science Dr. Merzenich research study involves the subjects being asked

questions from recorded narratives. The narratives are played slowly at first and progressively become

faster. The narratives are easy at first and progressively become difficult. The narratives are delivered via a computer-based training module

with minimal interaction with researchers. The level of challenge is crucial component in triggering brain

plasticity. The study was conducted with 95 older people aged 63-94. The exercises were: speed of processing, spatial syllable match

memory, forward word recognition span, working memory, and narrative memory.

The goal was for the subjects to train one hour a day for eight weeks.


Posit Science Results

– People who trained the full eight weeks significantly improved their scores on memory tests.

– People who progressed to the most difficult levels of the narratives showed the greatest improvements.

– Majority of participants gained ten neurocognitive years.– Exercise results:

Speed of Processing – 93% of participants improved by 41% Spatial syllable match memory – 77% of participants improved by 10% Forward word recognition span – 91% of participants improved by 18% Working memory – 80% of participants improved by 13% Narrative memory – 91% of participants improved by 18%


Posit Science Other disciplines affected: gerontology. Papers derived from this work:

– Brain plasticity and functional losses in the aged: scientific bases for a novel intervention

– Memory enhancement in healthy older adults using a brain plasticity-based training program: A randomized, controlled study


Brain-Gene Ontology Nikola Kasabov, Vishal Jain, and other authors from Auckland

University of Technology in New Zealand undertook the Brain-Gene Ontology (BGO) Project: mapping the relationship between the brain and the genes.

The goals of the BGO project, through a software application, are to find if these relationships can be used for further investigations in neuroinformatics and bioinformatics.

A side goal of the BGO project is that it can be used as a training tool for researchers and students.

The project was presented in the Sixth Annual Conference of Hybrid Intelligent Systems in December 2006 under the title: “Brain-Gene Ontology: Integrating Bioinformatics and Neuroinformatics Data, Information and Knowledge to Enable Discoveries.”


Brain-Gene Ontology BGO application consists of three parts:

– Brain organization and function – contains information about neurons, synapses and electroencephalogram (EEG) data for normal and epileptic brain states.

– Gene regulatory network – contains sections on neuro-genetic processing, gene expression regulation, protein synthesis, and abstract GRN.

– Simulation modeling – contains sections on computational neurogenetic modeling (CNGM), evolutionary computation, evolving connectionist systems, spiking neural network, simulation tools, and CNGM results

Snapshot of the BGO neuro-genetic simulation tool

Snapshot of the BGO detail showing relations between genes, proteins, neuronal functions and diseases

Neurons entering the brain: simulated activity

Snapshot signal propagation in neurons of the brain


Mitre Corporation – Human Brain Mapping Human brain mapping data (MRI, fMRI, Cryosection, EEG, etc.) is

rapidly accumulating worldwide (many terabytes)– but it is not widely shared– potential value of it’s scale is not being realized

Significant need for an appropriate information infrastructure. Our goal:

“The goal of this proposal is to enable the world-wide exploration, analysis, and dissemination of the growing corpus of human brain mapping information.”

Three basic architecture components:– digital library, associated repository, warehouse

Five basic workflows:– submission, retrieval, migration, definition, exploration

Overview Of Proposed System: 5 Processes

Warehouse

Digital Library

Submission1

Retrieval

2

Exploration- brain attributes- visualization- spatial reasoning- content-based retrieval

4

Migration3

AtlasGeneration

5

Data Archive Metadata Repositorygender

racetest score ....

Features Probabilistic AtlasesVolume

= 3.2

partitions

Process 1: Submission To Library

Data Archive (structural MRI

partition)

Metadata Repository

Mapping Datasurveytest (1)test (2)

etc.

core images

non-core

T1 T2 PD

tissue-labeled, scalped, normalized

noise (motion) corrected

reconstructed

race gender age test scores genetic info scan conditions etc....

Associated Metadata

Data Validation Tests

* (256 x 256 x 170 voxel matrix)

*

Process 2: Retrieval From Library

LRR*

* (Library Retrieval Request)

Selected Data

Apply Access Policy

Query

Data Archive (structural MRI

partition)Repository

core images

non-core

T1 T2 PD

tissue-labeled, scalped, normalized

noise (motion) corrected

reconstructed

race gender age test scores genetic info etc....

*

Process 3: Migration Into Warehouse

IndividualBrain

Object*: Labeled BrainVolume

Voxel-Label Anatomic Regions

Extract Features AndAnnotate Structure Hierarchy

StructuralBrain Hierarchy

FeatureAttributes

Warp To A Standard Space,(Generate Deformation Field)

Digital Library

Data WarehouseDeformation

Field

core images

+ +

T1 / tissue labeledbrain volume

* (One instance percore scanned brain)

Repository

ReplicateAssociatedMetadata

Process 4: Exploration Of Warehouse

Queries

SpatialReasoning

VisualizationContent-based

Retrieval“Extended”

Feature

Data Warehouse

Labeled BrainVolume Structural

Brain Hierarchy

FeatureAttributes

DeformationField

+ +IndividualBrain

Objects

Answers

StandardAttribute/Value

Describe Query

Optimization

OptionalLRR

QueryInterface

Process 5: Atlas Definition Within Warehouse

Data Warehouse

Labeled Probabilistic

Brain Volume


FeatureAttributes

Deformation Field OfPopulation CenterTo Standard Space

+ +CompositeBrain

Objects:

AtlasDefinition

gender = male

diseasestate

genotype

feature (e.g. hippocampalvolume size)

etc (extensible)

25 < age < 30

“fact table”

DescribeSubpopulationCharacteristics

Process 4 (revisited): Exploration (Atlases)

Queries

SpatialReasoning

PopulationComparison

Data Warehouse

Labeled Probabilistic

Brain Volume


FeatureAttributes

Deformation Field OfPopulation CenterTo Standard Space

+ +AtlasData

Model:

Answers

StandardAttribute/Value

Describe Query

Optimization

QueryInterface

Visualization

Current Work and Experiment

Brain Computer Interface Nick Chisolm is a man who became paralyzed in a rugby

accident at age 23 in 1998. He suffers from locked-in syndrome which is a condition where

you have lost almost all physical motion in the body but not the brain. The brain is still working at 100% efficiency.

Nick only had physical movement with his eyes. When he needed to compose a sentence or word, he had to use his eyes to indicate the validity of a letter of a word.

This rehabilitation process is time consuming and extremely frustrating for the victim.

His suffering prompted the work on BCI for paralyzed people.

Introduction

Brain-Computer Interface (BCI) is a device which allows the human to control electronic devices just by thinking.

Current BCIs are based on Electroencephalogram (EEG) .

Peirre Glorr and Hans Berger discovered EEG in 1969.

First BCI was built by Vidal in 1973 For more than 2 decades no real development was

done in BCIs, mostly waiting for the technology to catch up.

BCI- How does it Works?

– Amplify the EEG signals.– Digitize the signals. – Elimination of unwanted signals– Other necessary manipulation. – Translate the signals to computer commands.

BCI- Goes Wireless

Wearable or Wireless BCI is developed because of the advanced communication devices.

Wireless BCI interact with a PDA equipped with is the best visualization.

- Bluetooth – for portability– GPS -- to be aware of the environment – WLAN 802.11b– For access to the processing

power in Office/Home.

BCI- Wireless Visualization

BCI- Current Issues

BCI is interested only in EEG wavelets from the Cerebrum (Thinking Center) .Eliminating other wavelets like Electrooculogram (EOG) , Electromaygram (EMG), etc. is one issue.

Other Problems are:– Slow user response times– Excessive error rates– High cost– Actual appearance– Long initial training periods


Brain Computer Interface Paper from K. Navarro: “Wearable, Wireless Brain Computer

Interfaces In Augmented Reality Environments”– Current BCI does not currently follow design principles of the

Human Computer Interaction (HCI) discipline. BCI should use this knowledge and follow this “pattern language.”

– Author proposes the use of Augmented Reality Environments (AR) for the BCI wearer. Augmented Reality systems enhance the real world by superimposing information onto it. Ex: pair of glasses with information overlaid on the screen.

– Problem with making it reality: Developing a BCI for an AR environment addresses a specific problem. The goal of the BCI is to work in a highly changing environment.


Brain Computer Interface Dr. Jonathan Wolpow, Chief of Laboratory of

Nervous Systems Disorders in NYS Department of Health: Wadsworth Center spearheads an extraordinary BCI initiative.

Dr. Scott Mackler is one of his success stories. Dr. Mackler suffers from progressive neurodegenerative disease. He lost all movement in 1999.

With the help of Wolpow’s innovative approaches to BCI implementation, Dr. Mackler still goes to work.


Published papers from the Institute of Neuroinformatics for 2007: Fast sensory motor control based on event-based neuromorphic-

procedural systems The role of first and second order stimulus features of human overt

attention Modulation of synchrony without changes in firing rates Sleep-related spike bursts in HVC are driven by the nucleus

interface of the nidopallium Time and space are complementary in encoding dimensions in the

moth antennal lobe. Gamma range cortico-muscular coherence during dynamic force

output Implementing homeostatic plasticity in VLSI networks of spiking

neurons

Current Support

Human Brain Project Sponsored by the National Institute of Mental Health

of the National Institutes of Health Established in 1993 to support the research efforts in

neuroinformatics. Find new ways in spearheading neuroinformatics

research Develop informatics tools and resources for

neuroscience.

Current Support

Human Brain Project - Agenda of Annual Meeting – April 24, 2006 Current Initiatives:

– Improving Image Analysis Tools– Create physiological data and exploit using simulation– Creation of ASTYNAX: A pilot exploration of web technology

Problems with Data gathering:– Data

Data heterogeneity Lack of data standards Cultural gap requires paradigm shift

– Practice Few repositories available for willing stakeholders Information sparseness Lack of Incentive

Current Support

Insititute of Neuroinformatics Established in 1995 by the University of Zurich States that $1 trillion dollars is spent on neuroinformatics mosty

on communications, processing, and information management. Creating autonomous intelligent systems is slow.

Projects pursued within the institute are:– Behavior and Learning in Intelligent Autonomous Systems– Representation and Sensory Motor Integration– Neuronal Architectures and Computation– Neuromorphic Chips and Systems– Neurotechnologies

Current Support

Computational Neuroscience/Neuroinformatics Aims to unravel the complex structure-function relationships of

the brain at all levels from molecular to behavioral in an integrative effort with many scientific disciplines.

Based in Europe, the organization is one of the primary sponsors in conferences geared toward computational informatics. Some of these conferences are in United States and Canada as well.

– Sixteenth Annual Computational Neuroscience Meeting CNS, Toronto, Canada – July 2007

– MBX – Special Topic Courses – Neuroinformatics, Wood Holes, MA – August 2006

– Tenth Annual Conference on Cognitive and Neural Systems, Boston MA – May 2006

Current Support

NYS Department of Health: Wadsworth Center Under Dr. Jonathan Wolpow’s supervision, are working on

bring the BCI technology into home use. Streamlined version of the Wadsworth BCI consists of:

– laptop computer– portable amplifier– breathable cap which contains just 8 electrodes, down from the

original 64– currently about $4,000, but will price will drop as technology

improves Dr. Wolpow estimates that 70-80% with severe disabilities

could use the Wadsworth BCI System. Heavy funding from NIH for the next few years.

Links

• “Brain-Computer Interfaces Come Home.” National Institutes of Health: National Institute of Biomedical Imaging and Bioengineering. November 28, 2006. http://www.nibib.nih.gov/HealthEdu/PubsFeatures/eAdvances/28Nov06

• “The Brain Computer Interface with Natasha Mitchell.” AllInTheMind, ABC National Radio, Austraila. December 2, 2006. http://abc.net.au/rn/allinthemind/stories/2006/1799619.htm

• Computational Neuroscience/Neuroinformatics. http://www.hirnforschung.net/cneuro/

• The Human Brain Project. National Institutes of Health: National Institute of Mental Health. http://www.nimh.nih.gov/neuroinformatics/

Institute of Neuroinformatics. University of Zurich. http://www.ini.unizh.ch/public/ Mitre Corporation : Neuroinformatics website.

http://neuroinformatics.mitre.org/index.html Neuroinformatics. Wiki site. http://en.wikipedia.org/wiki/Neuroinformatics Neuroscience. Wiki site. http://en.wikipedia.org/wiki/Neuroscience Wadsworth Center: New York State Department of Health. Home page.

http://www.wadsworth.org/index.html

References

N. Kasabov, V. Jain, P. Gottgtroy, L. Benuskova, F. Joseph. “Brain-Gene Ontology: Integrating Bioinformatics and Neuroinformatics Data, Information and Knowledge to Enable Discoveries.” Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (HIS'06), pp. 13. December 2006.

H. Mahnke, A Bronstone, MM Merzenich. “Brain plasticity and functional losses in the aged: scientific bases for a novel intervention.” Journal of Progress in Brain Research. Volume 157. p. 81-109. 2006

H. Mahnke, B. Connor, J. Appelman, O. Ahsanuddin, J. Hardy, R. Wood, N. Joyce, T. Boniske, S. Atkins, M. Merzenich. “Memory enhancement in healthy older adults using a brain plasticity-based training program: A randomized, controlled study.” Procceedings of the National Academy of Sciences. August 23, 2006.

K. Navarro. “Wearable, wireless brain computer interfaces in augmented reality environments.” Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2, p. 643. April 2004

E. Singer. “Exercising the Brain: Innovative training software could turn back the clock on aging brains.” Technology Review, Massachusetts Institute of Technology, Cambridge, MA. November 21, 2005

Wearable, Wireless Brain Computer Interfaces In Augmented Reality Environments. By Karla Felix Navarro, University of Technology, Sydney IEEE 2004

P300 Detection for Brain-Computer Interface from Electroencephalogram Contaminated by Electrooculogram. By Motoki Sakai, Hiroyuki Ishita, Yuuki Ohshiba, Wenxi Chen, and Daining Wei Graduate School of Computer Science and Engineering, The University of Aizu, Ikki-machi, Aizu- Wakamatsu City, Fukushima 965-8580, Japan IEEE 2006

team 3: neuroinformatics

Documents

study of brain

brain function

human brain project

international neuroinformatics

growth of neuroinformatics

goals of neuroinformatics

subset of neuroinformatics

triggering brain plasticity