Neuroimagerie cognitive défis, enjeux - Bienvenue · Neuroimagerie cognitive défis, enjeux Bernard Mazoyer [email protected] ... •Marketing, decision making Philosophy

B Mazoyer – Neuroimagerie – Colloque Imagerie Bio-Santé– CNRS – 17 Juillet2014

Neuroimagerie cognitive

défis, enjeux

Bernard Mazoyer

[email protected]

Groupe d’Imagerie Neurofonctionnelle,

UMR5296, CEA, CNRS, UBxS

http://www.cnrs.gin.fr


Neuroimaging. Genesis

A i s ( ‘ ys ’ = viscus) + liquid secretion

brain

Edwin Smith Surgical papyrus, 1700 BC – Rare book room, New York Academy of Medicine


19th century: the anatomo-clinical approach

The « tan » case (1861) P. Broca (1824-1880)

« I make enquiries about the character,

vivacity, skills, …, and diseases of my patients

before they die; … I trustfully record all

informations I get. When the patient is dead, I

do the autopsy and I look carefully at the

brain … » V. Malacarne (1744-1816)

« Tell me where is fancie bred,

Or in the heart, Or in the head »

W.T. Shakespeare (1596)

The Merchant of Venice, III,64

1441

Cells and dualism

Neuroimaging. Cognitive and clinical


1980’s Physics

Detection/production of e.m.f.

(magnets/antennas, cristals/PMTs,)

Computer science Digital image acquisition & processing

(processors, allgorithms, memories) +

Neuroimaging. E.M. spectrum and techniques

Hz 1021 1018 1015 1014 1010 108 105 10 1 10-7

radio radar Visible light X e.m.f.

MEG EEG MRI OPT X-SCAN PET

eV 106 103 1 10-5 10-7 10-10 10-14 10-22

+ techniques ultra sonores et endoscopiques


Neuroimaging. Techniques and brain targets

HEMODYNAMIC IMAGING (vessels)

fMRI: functional magnetic resonance imaging

METABOLIC IMAGING (astrocytes, neurons)

PET: positron emission tomography (FDG)

MOLECULAR IMAGING (synapses)

PET (C11 or F11 labeled proteins)

ELECTROMAGNETIC IMAGING (membranes)

EEG/MEG: electro/magneto encephalography msec

STRUCTURAL IMAGING (grey matter, fibres)

T1-MRI: T1-magnetic resonance imaging (T1),

DTI: diffusion tensor imaging (DTI)


GIN, UMR5296 CNRS-CEA UBx

Neuroimaging. Techniques and brain targets: Functional Magnetic Resonance


BRAIN

/

MIND

Functional networks hemodynamic/metabolic imaging

fMRI, PET, NIRS

Who?

Structures anatomical imaging

MRI (T1), DTI

Where?

Neurotransmission neurochemical imaging

PET

How?

Chronometry, oscillations electromagnetic imaging

MEG, EEG

When?

Neuroimaging. Biological issues: WWWH


Science • Neuroscience: brain morpho-functional macroscopic architecture

• Psychology: brain/mind/cognition models

• Robotics: in silico brain

Education & training

• Updated and personalized pedagogy

• Enhanced skills through improved training methods (neurofeedback)

Forensic & Ethics

• Image-based evidence (brain disease, brain death, …): neuroimage in the court

• Neuroimage-based lie detection, brain decoding

Healthcare • Improving diagnostic, therapy evaluation, and prevention: imaging biomarkers

• Designing new treatments: image guided therapy, neurofeedback

Economy • Marketing, decision making

Philosophy • Theories, beliefs, religion

Neuroimaging. Issues


Understanding signal origin

• Mediators and modulators of hemodynamic signals

• Hemodynamic versus electromagnetic signals

• Multiscale integration

Multimodal integration • FMRI and DTI: functional and structural connectomics

• MRI & MEEG: spatio-temporal mapping

• FMRI/MEEG & PET: neurochemical cognitive neuroimaging

Neuroimaging. Challenges

Enhanced signal acquisition • Ultra high-field MRI (7T+): magnet, gradients, antennas

• New contrast agents and radiopharmaceuticals

Advanced image processing and statistical analysis • Segmentation, classification, pattern recognition, graph analysis

• Multivariate-highdimensional statistical analysis

• Data visualisation, and data mining

Identification/quantification of sources of brain phenotypes variability

• development, education, training, aging, genetic


3 current challenges/issues in neuroimaging

Imaging genetics Connectomics Variability sources





Variability sources


• Numerous

Genetic, age, sex, education, practice, lifestyle, life events, pathologies, …

• Phenotype dependent (or not?)

Behavior (exophenotype) versus morphology and function (endophenotypes)

Global versus local (target areas)

• Hard to estimate

Intrinsic higher variability than average statistics

Confounding factors

Needs large and balanced samples


Hemispheric specialization or

the embodiement of dualism and manicheism

“The murder of ABEL”

Marc Chagall, 1960

“Night of the hunter”

Charles Laughton, 1955 Advertisement for luxury cars, 2014

Identification of brain phenotype sources of variability


Anatomical asymmetries

Petalia & torque

(Toga 2003)

Global brain

asymmetry (85% in hominids

25% in great apes)

Rolando genu

(Rumeau et al, 1994)

Handedness and

manual skills

Left

hemisphere

Heschl gyrus and planum

temporale

(Geschwind & Levisty, 1968)

Language

Left

hemisphere

90% right-handed 90% left-hemisphere

dominant for language



SAMPLE 194 RH 80 LH

170 FS- 128 42

104 FS+ 66 38

(Tzourio-Mazoyer Cerebral Cortex 2009)

Left PT

(mm2) RH LH

FS- 668 704

FS+ 628 599

Right PT

(mm2) RH LH

FS- 470 480

FS+ 471 482

**

*

n.s. n.s.

n.s

.

MRI defined PT Handedness and structural marker of language

hemispheric dominance



Hemisheric Functional Lateralization Index

P(H

FLI)

Right-handed

Left-handed

Atyp Ambi Typ

12%

15% 7% 78%

88%

(Mazoyer PLoS One 2014)

FMRI derived language network

Left-handed enriched sample (BIL&GIN)

297 participants (153 left-handed)

P (HFLI) = p iN (mi,s i

2 )i=1

i=n

å

Hemispheric Functional Lateralization Index HFLIk= (Actgauche – Actdroite) / (Actgauche + Actdroite)

Multi-Gaussian modeling

Handedness and functional marker of language hemispheric dominance






T1 imaging

Diffusion imaging

Cortical thickness

Target VOI

WM

tractography WM

connectivity

GM density

WM density

3D morphology

Intrinsic

connectivity

(resting state)

F F F T T T T

Cognitive networks

(task related)

Effective/functio

nal connectivity

Functional

Structural

Connectivity. MRI-derived phenotypes


While you are listening,

No rest!

Activation of your speech listening

neural network

Recruitment of ~40 cc of cortex

5% increase of local blood flow and

glucose consumption

Stable local oxygen consumption

BOLD signal

Connectomics of the resting state (RS)


But: during the last hour, your brain

consumed:

• 5,4g of sugar and 4,5l of oxygen

•delivered by 45l of blood!

Q: What for?

A: For « sustaining » a background

brain activity named « brain default

mode », associated with a « mind in a

resting state » behaviour

Minor changes in your cerebral global metabolism:

•blood flow: +1 ml/min on top of 750 ml/min

•glucose consumption: +0,1 mg/min on top of 90 mg/min

While you are listening,

Connectomics. The resting state (RS) paradox

Goal directed activity

Intrinsic activity


Power spectrum

Connectomics. Brain networks during RS: intrinsic connectivity

Large scale networks with low frequency

synchroneous hemodynamic random fluctuations

Fox et al, PNAS, 2005

BIL&GIN database, N=282

BOLD fMRI, 8 min resting-state

Naveau, Neuroinformatics 2012

Doucet, Neuroimage 2012

http://mammouth.cyceron.fr/~gindeco/base/3tvs/t0001/resultats/bold01/repos/ica_a_m06/s6tmniwa.ica/filtered_func_data.ica/stats/melodic_tzmap/f33.txt


Connectomics. Modeling intrinsic functionnal connectivity

Doucet, Neuroimage 2012 Naveau, HBM 2012

Hub of degree

and betweeness


Connectomics. The new neuroimaging paradigm





Brain phenotype/genotype association studies

Imaging genetics. The multidimensional challenge

106 brain voxels x 107 SNP

= 1013 tests …

➞ very large sample > 105

subjects

➞ automated analysis

✽ high throughput

genome sequencing

✽ automated brain

morphometry

Medland et al2014


Cross-sectional multi consortium study

n = 9,234 for discovery + 2,322 for replication

Imaging genetics. Finding genes influencing hippocampus size

Bis et al. 2012


Nature Genetics 2012

Imaging genetics: finding genes influencing hippocampus size


SNP’s are from genes involved in

• apoptosis (HRK),

• embryo development (WIF1)

• oxydative stress (MSR3B)

Nature Genetics 2012

Imaging genetics: finding genes influencing hippocampus size


Groupe d’Imagerie Neurofonctionnelle , UMR5296

Scientists

Fabrice Crivello

Isabelle Hesling

Gael Jobard

Marc Joliot

Bernard Mazoyer

Emmanuel Mellet

Laurent Petit

Nathalie Tzourio-Mazoyer

Laure Zago

Postdocs

Pierre-Yves Hervé

V Budisavljevic

Technical staff

Anita Foro

Gaelle Leroux

Kristel Lozach’meur

Guy Perchey

PhD students

Quentin Duriez

Janice Hau

Damien Marie

Main collaborations

INSERM U708 (Neuroimaging cohorts)

MPI Nijmegen (Genetics of language)

ICBM consortium (Neuroimaging methods)

CHARGE consortium (Aging brain genetic biomarkers)

ENIGMA consortium (Genetics of brain asymmetry)

Documents

Neuroimagerie cognitive défis, enjeux - Bienvenue · Neuroimagerie cognitive défis, enjeux Bernard Mazoyer [email protected] ... •Marketing, decision making Philosophy