PAN Komitet nauk neurologicznych – WARSZAWA 2014
and how neuroimaging will contribute
Richard Frackowiak (CHUV & EPFL Lausanne) MOTIVATION 1 Alzheimer’s disease: 2 people out of 10 concerned beyond the age of 80; dependency occurs within 3 to 5 years after the disease has appeared.
Depression: the second most common condition in the world according to the WHO: it concerns 6 per cent of the population in the Western world.
Cerebral vascular accidents: the first cause of motor disabilities in adults. 75 per cent of victims suffer from residual disability.
Parkinson’s disease: second cause of motor disability. It affects 2 out of 1,000 people.
Multiple sclerosis: concerns mainly young people and leads to a loss of autonomy in 30 per cent of cases.
Epilepsy: 50 million people concerned in the world of which almost half bebefore age 10. The social and familial repercussions are lifelong.
CEA CHRU CNRS CPU INRA INRIA INSERM INSTITUT PASTEUR IRD CLINICAL NEUROSCIENCE AT A TIPPING POINT
SYNDROMIC DIAGNOSIS REACHED ITS LIMITS
HUMAN GENOME
MODERN NEUROIMAGING
MODERN NEUROSCIENCE
MODERN INFORMATION TECHNOLOGY
MODERN MATHEMATICS
MECHANISTIC/CAUSAL DIAGNOSIS CLINICAL SYNDROMES AND FINAL PATHOLOGY IN CASES BEGINNING WITH FTD
Kertesz et al, Brain 2005 ALZHEIMER’S DISEASE - DO WE NEED TO THINK AGAIN?
WHAT CAUSES IT?
What mechanisms? Role of genes? Abnormal proteins – amyloid? Abnormal neurotransmission – acetyl choline?
What pathophysiological abnormalities are causes and which effects?
HOW DO WE PREVENT IT? AND TREAT IT?
Can we diagnose it? - NO Do symptoms matter? - A LITTLE What weight to pathology? - END STAGE Do we compensate? - REDUNDANCY What about pre-symptomatic diagnosis? - ??? Why don’t the treatments work? - TREAT WHAT And what about preventive treatment? - ??? THE DECLINING INTEREST OF PHARMA MOTIVATION 2 - DATA FEDERATION & INTEGRATION
Number of Peer Reviewed Reality check Publications on the Brain /yr 1. Data and knowledge is growing 120000 exponentially 2. Data and knowledge is increasingly fragmented 100000 3. Benefits for society seem to be
decreasing (diagnostic accuracy, 80000 treatments, drugs) 4. Economic burden increasing rapidly to unsustainable levels 60000
What we lack 40000 1. No integration plan 2. No data curation plan 3. No plan to link across levels 20000 4. No plan to transfer knowledge from animal to human 0
0 1 2 3 5 6 7 8 9 0 1 2 3 5 6 7 8 9 0 1 5. No plan to go beyond symptom- 4 4
9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 0 0 0 1 1
9 0
9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 0
9 0 2012
1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 based classification of diseases 1 2 MOTIVATION 3 - INFORMATION TECHNOLOGY
VON NEUMANN MACHINES
MOORE’S LAW SUPERCOMPUTING
ENERGY LIMITATIONS BEYOND EXASCALE
INTERNET BANDWIDTH & ROUTING [HTML5, Cisco]
DATABASE MANAGEMENT DISTRIBUTED [Oracle]
CLOUD ENVIRONMENT SECURITY [Amazon, Dropbox, iCloud]
DATABASE QUERYING & ADDRESSING LOCAL [Google] vs REMOTE [EPFL]
REAL-TIME VISUALISATION FOR SUPERCOMPUTING [IBM, CRAY]
NEUROMORPHIC COMPUTING Future and Emerging Technology Flagships (FET)
Are ambitious large-scale, informatics-driven, research Initiatives that aim to achieve a visionary goal.
The scientific advance should provide a strong and broad Basis for future technological innovation and economic Exploitation in a variety of areas, as well as novel benefits for society.
The research is collaborative, internally non-competitive, inter- and trans-disciplinary, driven by a commonly agreed road-map
BLUE BRAIN PROJECT + NEUROIMAGING COMMUNITY Using modern technology to dramatically improve our understanding of the human brain
CLINICAL NEUROSCIENCE AT A TIPPING POINT
SYNDROMIC DIAGNOSIS REACHED ITS LIMITS
HUMAN GENOME BUILDING BLOCKS OF ORGANIC MATTER
MODERN NEUROIMAGING
MODERN NEUROSCIENCE
MODERN INFORMATION TECHNOLOGY
MODERN MATHEMATICS
MECHANISTIC/CAUSAL DIAGNOSIS HUMAN “NEURO-DISEASOME” – DISEASETHE DISEASEOME SPACE AS A FUNCTION OF GENETIC ASSOCIATIONS
THE POWER OF DATA MINING CLINICAL NEUROSCIENCE AT A TIPPING POINT
SYNDROMIC DIAGNOSIS REACHED ITS LIMITS
HUMAN GENOME BUILDING BLOCKS OF ORGANIC MATTER
MODERN NEUROIMAGING INCREASINGLY SOPHISTICATED
MODERN NEUROSCIENCE
MODERN INFORMATION TECHNOLOGY
MODERN MATHEMATICS
MECHANISTIC/CAUSAL DIAGNOSIS PRE-SYMPTOMATIC DIAGNOSIS - BRAIN RESERVE
HUNTINGTON’S DISEASE
COMPENSATEDHUNTINGTON’S ATROPHY COMPUTER-BASED IMAGE CLASSIFICATION
SUPPORT VECTOR MACHINE CLASSIFICATION PARADIGM SHIFT – BRAIN TISSUE CHARACTERISATION (VBQ) T1-weighted Thickness/Volume ? Statistical Interpretation inferences
Quantitative and diffusion MRI
Magnetisation Fractional Proton density R1 (1/T1) R2* (1/T2*) Mean diffusivity transfer anisotropy ! Interpretation
Water Myelin Water motility Iron White matter « integrity »
by A. Ruef DISEASE MODELLING AND TREATMENT PREDICTION
Structural MRI and FDG-PET
. GLM1: Establishing a voxelwise healthy aging model
. GLM2: Modelling of Alzheimer’s disease related changes at different ages (orthogonalized for GLM1)
Dukart et al., 2013, PLoS Comp Biology BASAL GANGLIA ARCHITECTURE
A review of the entire tract-tracing literature of the STN between 1947- 2011 reveals connectivity between a broad array of cortical, sub-cortical and brainstem structures.
BLUE = EFFERENT RED = AFFERENT
Lambert et al., Confirmation of functional zones within the human subthalamic nucleus: Patterns of connectivity and sub-parcellation using diffusion weighted imaging NeuroImage Volume 60, 2012 83–94 FUNCTIONAL IMPLICATIONS
The posterior aspect of the STN projects to structures consistent with a motor structure: Posterior putamen Posterior GPe Mid caudate nucleus Ventro-lateral thalamic nuclei Posterior Insula Posterior hippocampus
The anterior aspect of the STN projects to structures consistent with a limbic structure: Baso-lateral amygdala Posterio-medial GPi Inferio-mid putamen Mid-GPe Ventral-anterior and ventral-lateral thalamus Anterior Insula Anterior hippocampus
The middle “associative” STN projects to regions encompassing both the motor and limbic projections CLINICAL NEUROSCIENCE AT A TIPPING POINT
SYNDROMIC DIAGNOSIS REACHED ITS LIMITS
HUMAN GENOME BUILDING BLOCKS OF ORGANIC MATTER
MODERN NEUROIMAGING INCREASINGLY SOPHISTICATED
MODERN NEUROSCIENCE FRAGMENTED AND ATHEORETIC
MODERN INFORMATION TECHNOLOGY
MODERN MATHEMATICS
MECHANISTIC/CAUSATIVE DIAGNOSIS NEUROSCIENCE METHODS IMAGES – IMAGES - IMAGES CLINICAL NEUROSCIENCE AT A TIPPING POINT
SYNDROMIC DIAGNOSIS REACHED ITS LIMITS
HUMAN GENOME BUILDING BLOCKS OF ORGANIC MATTER
MODERN NEUROIMAGING INCREASINGLY SOPHISTICATED
MODERN NEUROSCIENCE FRAGMENTED AND ATHEORETIC
MODERN INFORMATION TECHNOLOGY EXPLOSIVE DEVELOPMENT
MODERN MATHEMATICS
MECHANISTIC/CAUSATIVE DIAGNOSIS MEDICINE AT A TIPPING POINT
SYNDROMIC DIAGNOSIS REACHED ITS LIMITS
HUMAN GENOME BUILDING BLOCKS OF ORGANIC MATTER
MODERN NEUROSCIENCE FRAGMENTED AND ATHEORETIC
MODERN CLINICAL NEUROSCIENCE INCREASINGLY SOPHISTICATED
MODERN INFORMATION TECHNOLOGY MOORE’S LAW BUT ENERGY LIMITED
MODERN MATHEMATICS FACILITATED BY CALCULATION POWER
DISEASE SIGNATURES MECHANISTIC DIAGNOSIS THE HUMAN BRAIN PROJECT SIMULATING AND CONSTRUCTING A BLUEPRINT FOR THE BRAIN AT ALL SPATIAL SCALES FROM BASE PAIRS TO COGNITION MODELING: EQUATIONS FOR CELLULAR LEVEL BRAIN SIMULATIONS NEURONS
10,000 neurons ~ 4,000,000 Electrical compartments (Rall Equations)
ION CHANNELS
80,000,000 Ion Channels
(Hodgkin-Huxley Equations)
SYNAPSES
10,000,000 Synapses
(Tsodyks-Markram Equations)
Tsodyks & Markram, PNAS,1997 NEURONS 10,000 neurons ~ 4,000,000 Electrical compartments (Rall Equations)
ION CHANNELS
80,000,000 Ion Channels
(Hodgkin-Huxley Equations)
SYNAPSES
10,000,000 Synapses
(Tsodyks-Markram Equations) MODELS ARE DATA DRIVEN
Markram et al., In vitro MODELS ARE MATHEMATICALLY EXPRESSED In silico Nature Reviews MODELS ARE BIOLOGICALLY VALIDATED Neuroscien ce, 2004 A SIMULATED PYRAMIDAL NEURON MODELLED LFPs IN A CORTICAL COLUMN MODELLED MULTI-COLUMNAR PATCH PROPAGATION Neural Computation: Causal Chain of Events leading to Cognition Processor
Sensory Input Motor Output
Sensing, Learning, Memory, Adaptation, Decisions, Cognition, Behavior MEDICAL INFORMATICS PLATFORM
912 Alzheimer’s patients MRI data 5566 Healthy controls MoAE PET data MODALITIES Gene data CSF data Protein data
PET CSF
Organising Processing… Tabulating
MRI
CLINICAL SCALES & MEASUREMENTS PROTEINS Genes HYPERCUBE
Scientist Internet Computer architecture
Launch HyperCube Separate dedicated Data Source Learning matrix calculation network
HyperCube interface •SAS •Oracle •Data Audit •Access •Correlations •Excel •Rule Builder •Extraction of internal or external data •…
•Rule management •Scenario simulation
Set of rules
Automatic rule recovery 37 AD Rule NL Rule MRI Data PET Data Proteomics CSF Genetics Principles of Reconstructing Simulating and Reverse Engineering the Human Brain
Brain Atlases Biological Parameter Constraints & Biological Principles Multi-constraint Algorithms Data source Configurations Brain Reconstruction Workflows
Measured Derived Manual Disease Measured Volumizer Vacularizer Segmenter Measured Derived Manual Disease Measured Volumizer Vacularizer Segmenter Measured Derived Manual Disease Measured Volumizer Vacularizer Segmenter Measured Derived Manual Disease Measured Volumizer Vacularizer Segmenter Bouton Disease DiseaseDistributor Tracter Modularizer Data Principles Volumizer Vacularizer Segmenter densityBouton Data Principles Distributor Tracter Modularizer densityBouton Distributor Tracter Modularizer densityBouton Tracter Modularizer Synthesizer 1 Distributor Synthesizer 2 Synapse densityBouton (morphologi Distributor Tracter Connector Modularizer Synthesizer 1 (projections) es) Synthesizer 2 densitySynapse density A AA A AA (morphologi Connector Synthesizer 1 (projections) es) Synthesizer 2 densitySynapse (morphologi Connector Synthesizer 1 (projections) es) Synthesizer 2 densitySynapse (morphologi Connector Synthesizer 3 (projections) Syns/con density Synthesizeres) Synthesizer 1 4 SynthesizerSynthesizer 2 5 Synapse (extracellular Connector Synthesizer 3 (morphs) (glia) (projections)(synapses) nectSyns/con density B BB B BB ) Synthesizer 4 Synthesizer 5 (extracellular Synthesizer 3 (glia) (synapses) nectSyns/con ) Synthesizer 4 Synthesizer 5 (extracellular Synthesizer 3 (glia) (synapses) nectSyns/con ) Synthesizer 4 Synthesizer 5 (extracellular Synthesizer 6 (glia) (synapses) P nectSyns/con SynthesizerMolecularize 3 SynthesizerBiochemicali 4 Synthesizer 5 (intracellular ) Synthesizer 6(extracellular) r (glia) zer (synapses) ConnectP nect C C CC ) Molecularize Biochemicali (intracellular Synthesizer 6 r zer ConnectP ) Molecularize Biochemicali (intracellular Synthesizer 6 r zer ConnectP ) Molecularize Biochemicali (intracellular Functionalize r zer Axonal ConnectP Synthesizer) 6 Biochemicaliz r Molecularizer densityAxonal Connect D … (intracellular)Functionalize er r densityAxonal Functionalize r densityAxonal Functionalize densitySynaptic r Respons E … Functionalizer e THREE RESEARCH AREAS FUTURE CLASSIFY MEDICINE
FUTURE FUTURE NEUROSCIENCE COMPUTING
UNIFY PRODUCE OPEN SCIENCE & EDUCATION
The platforms are designed for openness 1. Open to new partners from the start 2. Budget builds up to nearly 50% for Open Science & Technology 3. Foster a new generation of multi-disciplinary scientists, clinicians & engineers THANKS FOR LISTENING
FIL, London LREN, Lausanne
John Ashburner Ferath Kherif Nik Weiskopf Jürgen Dukart Renaud Marquis Anne Ruef Maria Knyazeva Valérie Beaud Antoine Lutti Valérie Zufferey Sandrine Muller EPFL, Lausanne Stas Adaszewski Henry Markram JF Demonet Sara Lorio University of Heidelberg Karlheinz Meier www.unil.ch/lren www.humanbrainproject.org