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Qu’est-ce que l’IRM c érébrale A quoi sert-elle, Quelles sont ses limitations ?. Oury Monchi, Ph.D. Centre de Recherche, Institut Universitaire de Gériatrie de Montréal & Universit é de Montréal. Plan du cours. 12 conf érences de 3 heures 5 travaux pratiques de 3 heures devant ordinateurs

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qu est ce que l irm c r brale a quoi sert elle quelles sont ses limitations

Qu’est-ce que l’IRM cérébraleA quoi sert-elle, Quelles sont ses limitations?

Oury Monchi, Ph.D.

Centre de Recherche, Institut Universitaire de Gériatrie de Montréal & Université de Montréal

plan du cours
Plan du cours
  • 12 conférences de 3 heures
  • 5 travaux pratiques de 3 heures devant ordinateurs
  • 1 examen devant ordinateur (30%)
  • 1 examen écrit (70%)
  • (3 crédit)
conf rences i
Conférences I
  • A quoi sert l’IRM cérébrale? (maintenant!)

6. Introduction à l’analyse de l’IRMf et aux méthodes de manipulation des images. (13 février)

2. Introduction aux contrastes d’IRM. Dr. Rick Hoge (16 janvier)

3. Reconstructions d’images. Dr. Rick Hoge (23 janvier)

4. Devis expérimentaux, hypothèses, software de

présentation (30 janvier)

5. Vérifications des données, Prétraitement (6 février)

conf rences ii
Conférences II

Mise en œuvre des analyses et applications. avec Mathieu Desrosier) (20 février)

Normalisation des données. (27 fevrier)

Méthodes d’IRM anatomique : VBM, DTI et MT. Dr. Thomas Jubault (12 mars)

Connectivité fonctionnelle et anatomique. Dr. Keith Worsley (19 mars)

Examples d’applications en neurosciences cognitives (26 Mars)

Session No 12 : Fusions de données et applications cliniques.

Dr. Claude Kaufmann (2 Avril)

Examen final écrit (70%): Mercredi 16 avril de 13.30 à 16.30

ateliers informatiques thomas jubault ph d et claudine gauthier m sc
Ateliers informatiques(Thomas Jubault, Ph.D et Claudine Gauthier, M.Sc.)
  • 7 février : Vérification des données et pré-traitement
  • 14 février: Modèle Linéaire Générale
  • 21 février: Moyennage et Normalisation
  • 13 mars: Visualisations et tables de résultats
  • 20 mars: Introduction à d’autres méthodes
  • 27 mars: Examen pratique (données à analyser)
techniques d irm
Techniques d’IRM
  • I. Imagerie par Résonance Magnétique (IRM)
  • A. Etudes anatomiques
  • B. Etudes Fonctionnelles
  • C. Etudes Physiologiques
slide14

Principes de base de l’IRM

Aimant: Champ magnétique très puissant (1 à 7T) et homogène

qui va inciter les protons d’hydrogène à s’aligner.

Champ magnétique de la terre 0.00005T!

Bobine de radiofréquence: envoie une impulsion à la fréquence

de résonance de l’hydrogène. Après être entrer en état de résonance

ces protons vont revenir à leur état de base à une vitesse différente

suivant le tissue dans lequel il se trouve.

Ceci générera un contraste de type T1

Bobine de gradients: le signal généré par la RF ne nous donnent

pas d’information spatiale en temps que tel, ce sont les bobines de

gradients alignées sur trois axes (x, y, z) qui nous permettent de

le faire.

slide15

IRM: Principes de Base

  • Spins des protons d’Hydrogène
slide16

IRM: Principes de Base

  • Spins des protons dans le champ statique B0
slide17

IRM: Principes de Base

  • Effets de radiofréquences en résonance
slide18

IRM: Principes de Base

  • Combinaison de radiofréquences et gradients = localisation spatiale des coupes de l’objet
slide19

IRM: Principes de Base

  • Temps de relaxation des spins (T1 et T2
slide20

IRM: Principes de Base

  • Temps de relaxation de T1 et T2
slide22

IRM: Principes de Base

  • Gradients X, Y, Z, Shim.
slide24

R N Q /U N F

Research programs: Innovations

MRI Methods

voxel based morphometry
Voxel Based Morphometry

Voxel based morphometry (VBM) is a neuroimaging analysis technique that allows investigation of focal differences in brain volume.

Traditionally, brain volume is measured by drawing regions of interest (ROIs) and calculating the volume enclosed. However, this is time consuming and can only provide measures of large areas. Smaller differences in volume may be overlooked. VBM registers every brain to a template, which gets rid of most of the large differences in brain anatomy among people. Then the brain images are smoothed so that each voxel represents the average of itself and its neighbors. Finally, volume is compared across brains at every voxel.

voxel based morphometry1
Voxel Based Morphometry

Brenneis et al., 2004

JNNP

slide29

a

g

b

c

f

e

Anatomie: Tenseurs de diffusion

Étude de la connectivite anatomique

slide33

Principes de base de l’IRMf

On connait une relation entre l’activité cérébale et le taux d’hémoglobine désoxygéné dans le sang

Début des années 90 il a été découvert qu’une séquence d’impulsions produites par l’IRM pourrait mesurer le taux

d’hémoglobine désoxygéné (Thulborn et al.; Ogawa et al.)

Ceci a donné naissance au Blood Oxygenation Level Dependent (BOLD) fMRI ou T2* qui nous donne une mesure indirecte de l’activité cérébrale.

slide40

Experimental design

  • Block design
  • compare long periods (e.g., 16 sec) of one condition with long periods of another
  • traditional approach
  • most statistically powerful approach
  • less dependent on how well you model the hemodynamic response
  • Event-related design
  • compare brief trials (e.g., 1 sec) of one condition with brief of another
  • very new (since ~1997) approach
  • less statistically powerful but has many advantages
  • trials can either be well-spaced to allow the MR signal to return to baseline between trials (e.g., 12+ seconds between trials) or closely spaced (e.g., every 2 sec)
slide43

Modeling the data (GLM)

(From Dr. J. Armony)

slide48

IRMf: Principes de Base

  • Variations de la réponse hémodynamique
slide52

Imagerie Optique

La technique est basé sur l’émission d’un faisceau lumineux dans le cerveau à des fréquences proches de l’infra-rouge

L’absorption de ce faisceau nous donne de l’information

sur l’oxygénation et la désoxygénation du sang similairement à l’IRMf.

La diffusion de ce signal nous donnent de l’information spatiale

imagerie optique pilepsie
Imagerie optique: Épilepsie

Diagnostic de l’épilepsie

science sans conscience n est que ruine de l me francois rabelais
Science sans conscience n’est que ruine de l’âme! (Francois Rabelais)

Une éxperience sans question ou hypothèse

ne sert pas à grand chose et peut être couteuse!

L’important c’est la question, si l’IRMf peut y répondre, il faut savoir faire des dessins éxpérimentaux les plus appropriés

irmf chez diff rentes populations
IRMf chez différentes populations
  • Attention: Différences d’activité neuronal, ou différences dans le métabolisme de désoxyhémoglobine
  • Heureusement certains chercheurs essaient de répondre à cette question, p.ex. Dr. Rick Hoge au CRIUGM
mauvaise tude 1

T-stat

Caudate

5

2.5

X = 12

Mauvaise étude 1!

Voici l’aire de l’éternuement!

slide64

functional MRI: Voice recognition

Belin, et al. (2000) Nature

mauvaise tude 2
Mauvaise étude 2!

Voici le réseau de l’attention!

slide66

Théorie proposée

Comparaison/

Sélection

Association

stimulus/action

Monitoring/

association

Niveaux

Organisation

dans la mémoire

de travail

CORTEX

Préfrontal

Dorsal

9, 46

Ventral

47/12

Postérieur

int 6, 8, 44

Planification,

sélection ou exécution d’une nouvelle action

Caudé ventral

Caudé dorsal

STRIATUM

Putamen

slide67

vs

  • Retrieval w/ shift

Montreal Card Sorting Task, Étude I

  • Retrieval w/o shift

Cue card

Prédictions

avec changement: CPF-VL+ Noyau caudé

sans changement: CPF-VL, PAS de striatum

Monchi et al., Ann. Neurol.,2006

slide68

Montreal Card Sorting Task

  • Changement de règle continu

Matching according to number

Matching according to colour

  • Condition contrôle

Prédictions

Changement de règle continu: CPF-VL, PAS de striatum

slide69

IRMf MCST: Contrôles en santé

No striatum

VL-PFC

Caudate

T-stat

7

VS

VL-PFC

Retrieval WITH shift

Control

3

Cue Card

No striatum

Cue Card

VL-PFC

VS

Continuous shift

Control

VS

Retrieval NO shift

Control

X = 18

Y = -4

Monchi et al. Feb 2006, Annals of Neurology

slide70

fMRI MCST: Healthy Controls

Putamen

T-stat

Caudate

5

3

X = 12

T-stat

Caudate

5

Cue Card

Cue Card

Cue Card

2.5

X = 12

VS

Retrieval NO shift

Retrieval WITH shift

VS

Continuous shift

Retrieval WITH shift

Le noyau caudé n’est pas particulièrement impliqué dans le changement de règle en soi, mais dans la planification d’une nouvelle action.

Monchi et al. Feb 2006, Annals of Neurology

experimental design
Experimental Design
  • Design specification
    • Number of blocks, trials or experimental units per session and/or subject.
    • Length of each trial, and interval between trials
      • If variable interval, report the mean and range of ISIs and how they were distributed.
    • Blocked designs:
      • Number of blocks
      • Length of blocks
    • For event-related fMRI, was efficiency optimization used, and if so, how?
    • For mixed designs, report correlation between block and event regressors
experimental design1
Experimental Design
  • Task specification
    • What were subjects asked to do?
    • What were the stimuli?
    • Did specific stimuli repeat across trials?
human subjects
Human subjects
  • Details on subject sample
    • Number of subjects
    • Age (mean and range)
    • Handedness
    • Number of males/female
    • Additional inclusion/exclusion criteria, if any
    • If any subjects were scanned but then rejected from analysis after data collection, state how many and reasons for rejection
    • For group comparisons, what variables were equated across groups
  • Ethics approval
    • state which IRB approved the protocol
data acquisition
Data Acquisition
  • Image properties - As acquired
    • MRI system:
      • Manufacturer, field strength (in Tesla), model name
    • MRI acquisition:
      • Number of experimental sessions and volumes acquired per session
      • Pulse sequence type (gradient/spin echo, EPI/spiral)
      • Field of view, matrix size, slice thickness, interslice skip
      • Acquisition orientation (axial, sagittal, coronal, oblique; if axials co-planar w/ AC-PC, the volume coverage in terms of Z in mm)
      • Whole brain? if not, state area of acquisition
      • Order of acquisition of slices (sequential or interleaved)
      • TE/TR/flip angle
data acquisition1
Data Acquisition
  • Pre-processing: General
    • Specify order of preprocessing operations
    • Slice-timing correction
      • minimally, software version; ideally, order and type of interpolation used and reference slice
    • Motion correction
      • software version (major and minor version numbers)
      • Interpolation method (also ideally, image similarity metric and optimization method)
      • Motion-susceptibility correction?
data acquisition2
Data Acquisition
  • Pre-processing: Intersubject registration
    • Intersubject registration method used.
      • Software version
      • Transformation model
        • Linear
        • - Number of parameters
        • Nonlinear
        • - Nature of deformation and number of parameters (E.g. in AIR, a polynomial order is specified; in SPM, a DCT basis size is specified, 3x2x3).
        • - Non-linear regularization? (E.g. in SPM, e.g. "a little"). Crucial for fluid-deformation methods
      • Interpolation method
data acquisition3
Data Acquisition
  • Pre-processing: Intersubject registration…
    • Object Image information. (Image used to determine transformation to atlas)
      • Anatomical MRI? Image properties (see above).
      • co-planar with functional acquisition?
      • Functional acquisition co-registered to anatomical? if so, how?
      • Segmented grey image?
      • Functional image (single or mean)
data acquisition4
Data Acquisition
  • Pre-processing: Intersubject registration…
    • Atlas/target information
      • Brain image template space, name, modality and resolution.
        • (E.g. "SPM2's MNI, T1 2x2x2"; "SPM2's MNI Gray Matter template 2x2x2")
      • Coordinate space?
        • Typically MNI, Talairach, or MNI converted to Talairach
        • If MNI converted to Talairach, what method? E.g. Brett's mni2tal?
      • How were anatomical locations (e.g. Brodmann areas) determined? (e.g. paper atlas, Talairach Daemon, manual inspection of individuals' anatomy, etc.)
data acquisition5
Data Acquisition
  • Pre-processing: Smoothing
    • What size smoothing kernel?
    • What type of kernel (especially if non-Gaussian, or adaptive).
    • Is smoothing done separately at 1st and 2nd levels?
statistical modeling
Statistical Modeling
  • General issues
    • For novel methods that are not described in detail in a separate paper, provide explicit description of method either in the text or as an appendix
  • Intrasubject fMRI Modeling Info
    • Statistical model and software version used (e.g. Multiple regression model fit with SPM2, updates as of xx/xx/xx; or FSL release 3.3).
    • Block or event-related model
    • Hemodynamic response function (HRF) assumed or estimated? If HRF used, which (e.g. SPM's canonical dual-gamma HRF; SPM's gamma basis; Gamma HRF of Glover).
statistical modeling1
Statistical Modeling
  • Intrasubject fMRI Modeling Info…
    • Additional regressors used (e.g. motion, behavioral covariates)
    • Drift modeling (e.g. DCT with cut off of X seconds; cubic polynomial)
    • Autocorrelation modeling (e.g. for SPM2, 'Approximate AR(1) autocorrelation estimated at omnibus F-significant voxels (P<0.001), then pooled over whole brain'; for FSL, 'Regularized autocorrelation function estimated at each voxel').
statistical modeling2
Statistical Modeling
  • Intrasubject fMRI Modeling Info…
    • Estimation method: OLS, OLS with variance-correction (G-G correction or equivalent), or whitening.
      • Tom Nichols: Is this too hard core? It's what I want to know, but I guess you could argue that given enough detail about the software it could be inferred.
    • Contrast construction. Exactly what terms are subtracted from what. It might be useful to always define abstract names (e.g. AUDSTIM, VISSTIM) instead of underlying psychological concepts.
statistical modeling3
Statistical Modeling
  • 2-level, modality-generic Modeling Info
    • Statistical model and software version used (e.g. 1-sample t on intrasubject contrast data, SPM2 with updates as of xx/xx/xx).
    • Whether first level intersubject variances are assumed to be homogeneous (SPM & simple summary stat methods: yes; FSL: no).
    • If multiple measurements per subject, method to account for within subject correlation. (e.g. SPM: 'Within-subject variance-covariance matrix estimated at F-significant voxels (P<0.001), then pooled over whole brain'). Jesper Andersson request: Variance correction corresponding to within-subject variance-covariance matrix, so simply some measure of nonsphericity.
statistical modeling4
Statistical Modeling
  • 3rd-level group difference modeling info
    • Statistical model and software version used (if different from 1/2) (e.g. 2-sample unpaired t on contrast images).
statistical inference
Statistical Inference
  • Inference on Statistic Image (thresholding)
    • Type of search region considered, and the volume in voxels or CC. If not whole brain, how region was found; method for constructing region should be independent of present statistic image.
    • If threshold used for inference and threshold used for visualization in figures is different, clearly state so and list each.
    • All inferences must explicitly state if they are corrected for multiple comparisons, and if so, what method and over what region.
      • If correction is limited to a small volume, the method for selecting the region should be stated explicitly.
      • If no formal multiple comparisons method is used, the inference must be explicitly labeled "uncorrected".
statistical inference1
Statistical Inference
  • Inference on Statistic Image (thresholding)…
    • There was some disagreement over this topic in the discussion group. While most of the discussants felt that it should be acceptable to report uncorrected statistics in some cases, there was also widespread feeling that current reporting standards are too loose. The following comments provide a view of the range of opinions:
      • Keith Worsley:
      • I've always advocated doing both i.e. labelling the 'confirmed' ones with a corrected P-value < 0.05, and reporting anything else as 'unconfirmed' or speculative or suggestive etc. (like PCA/ICA - but we're working on this!) - as long as it's clear, I don't see that it matters too much
statistical inference2
Statistical Inference
  • Inference on Statistic Image (thresholding)…
    • Tom Nichols:

While requiring some sort of correction, as Nature Neuroscience does, is a very practical stance, it seems to temp a fishing for a correction (e.g. find the right SVC until you have significance).

Following on Keith's comment, I think it could be best to simply require: all inferences must explicitly state if they are corrected for multiple comparisons, and if so, what method and over what region.

If no formal multiple comparisons method is used, the inference must be clearly labeled "uncorrected".

This hopefully would put implicit pressure on people to use corrected methods, but if someone wants to report "p<0.001" uncorrected, or, even as Matthew suggested, p<0.01, so be it, if they can convince the reviewers that it's compelling. As long as inferences are clearly labeled, I think people can do what they want. (Liberal inferences will be seen as weak evidence, and that's that.)

statistical inference3
Statistical Inference
  • Inference on Statistic Image (thresholding)…
    • Matthew Brett:

I absolutely agree we need the freedom to report effects that are weak. For me the point is that using an uncorrected p value as an index of this effect is bad statistics. The uncorrected p value is meaningless as an attempt at some sort of type 1 error control, depending as it does on a large number of factors including smoothing, df, field of view and so on. You p<0.001 is not comparable to someone else's p<0.001. The uncorrected p value then gives a spurious impression of statistical rigor.

If an effect is weak, and you want to state that it is close to being significant, then the two options that seem sensible to me are 1) drop the corrected threshold to - say - 0.1, or 2) show the effect size map and argue your case.

To me it is unfortunate that lots of young researchers in FMRI seem to believe that uncorrected p<0.001 is in some magical sense 'sort of significant' - and it's such an unreliable rule of thumb, and so confusing for people entering the field, that we should really try and move away from that.

statistical inference4
Statistical Inference
  • ROI Analysis
    • How were ROI's defined (e.g., functional versus anatomical localizer)?
    • How was signal extracted within ROI?
remerciements
Remerciements
  • Jorge Armony, Ph.D.
  • Claudine Gauthier, M.Sc.
  • Rick Hoge, Ph.D.
  • Julien Doyon, Ph.D.

Pour obtenir les diapos:

http://unfweb.criugm.qc.ca/oury/downloads.html

slide92

Référence IRMf recommendée

Sinauer Associates

Publisher