41

Définitions - ADDICTION - DIAPOR… · Définitions •Addiction: consommation compulsive en dépit de toutes conséquences négatives •Dépendance: survenu d’un syndrome de

  • Upload
    donhu

  • View
    218

  • Download
    0

Embed Size (px)

Citation preview

Définitions

• Addiction: consommation compulsive en dépit de toutes conséquences négatives

•Dépendance: survenu d’un syndrome de sevrage à l’arrêt brusque de la drogue

Casen millions

CoûtsMia€/an

Dépression 21 104Addiction (sans nicotine) 9 57Demence (Alzheimer) 5 55Troubles anxieux 41 41Schizophrénie 3.5 35Migraine 41 27AVC 1 22Epilepsie 3 15Maladie de Parkinson 1.2 11

2006

Maladies du cerveau en Europe

• Hallucinogènes (LSD) 1

• Cannabis (THC) 2

• Benzodiazépines (BDZ) 2

• Alcool 3

• Nicotine 3

•Opiacés (Morphine, Héroïne) 4

• Amphétamines 5

• Cocaïne 5

Risque relatif d’addictionGoldstein & Kalant, Science 1990

20

15

10

5

0

Cum

ulat

ive

proa

bilit

y (%

)403020100

Years since first use

Addiction vs. consommation récréative

Cocaïne

Cannabis

Wagner & Anthony, Neuropsychopharmacology, 2002

Cum

mul

ativ

e pr

obab

ility

(%)

Années depuis première consommationPr

obab

ilité

cum

ulativ

e (%

)

Cocaïne

100

80

60

40

20

0

Cum

ulat

ive

proa

bilit

y (%

)403020100

Years since first useC

umul

ativ

e pr

obab

ility

(%)

80%

Années depuis première consommationPr

obab

ilité

cum

ulativ

e (%

)

Wagner & Anthony, Neuropsychopharmacology, 2002

Addiction vs. consommation récréative

Cannabis

Dangers de la cocaïne

• Pression sanguine ↑↑

• Infarctus du myocarde

• Arythmie cardiaque

• Hémorragie cérébrale

• Addiction

Vulnérabilité individuelle ?

Nicotine Gambling ChocolateCocaine Morphine

AddictionAddiction

-----------

Drugs Gambling Food

Leyton et al., 2002 Boileau et al., 2003 Small et al., 2003

e.g.Poker e.g.Hamburgere.g.Amphetamines

Activation of the ventral striatum

-----------

Drugs Gambling Food

Leyton et al., 2002 Boileau et al., 2003 Small et al., 2003

Activation of the ventral striatum

e.g.Poker e.g.Hamburgere.g.Amphetamines

Mesolimbic DA system

Ventral tegmental area (VTA)

Accumbens

DA

GABA

NAcVTA

i

DA

GABA

NAcVTA

i

Increased dopamine(all addictive drugs)

Class I (morphine, THC, GHB)

G

G

Ventral tegmental area

Dopamine transporterG-protein coupled receptor Ionotropic receptorLüscher & Ungless, PLoS, 2006

DA

GABA

NAcVTA

i

DA

GABA

NAcVTA

i

Increased dopamine(all addictive drugs)

Class I (morphine, THC, GHB)

G

G

DA

GABA

Class II (benzodiazepines, nicotine, ethanol)

NAcVTA

i

i

Increased dopamine(all addictive drugs)

Class I (morphine, THC, GHB)

G

G

Ventral tegmental area

Dopamine transporterG-protein coupled receptor Ionotropic receptorLüscher & Ungless, PLoS, 2006

DA

GABA

NAcVTA

i

DA

GABA

Class III(cocaine, amphetamine, ecstacy)

Class II (benzodiazepines, nicotine, ethanol)

NAcVTA

i

i

T

Increased dopamine

Class I (morphine, THC, GHB)

G

G

DA

GABA

NAcVTA

i

Increased dopamine(all addictive drugs)

Class I (morphine, THC, GHB)

G

G

DA

GABA

Class II (benzodiazepines, nicotine, ethanol)

NAcVTA

i

i

Increased dopamine(all addictive drugs)

Class I (morphine, THC, GHB)

G

G

Ventral tegmental area

Dopamine transporterG-protein coupled receptor Ionotropic receptorLüscher & Ungless, PLoS, 2006

Classe III-drogues

Nicotine Gambling ChocolateCocaine Morphine

Addiction

Mesolimbic dopamine ↑

Addiction

0 1 2s

Schultz et al., Science 1997

Prediction error

-1 0 1 2s

0 1 2 s

-1 0 1 2s

R

0 1 2s

Schultz et al., Science 1997

0 1 2 s

-1 0 1 2s

no R

0 1 2

Image

-1 0 1 2s

Image R

0 1 2

R

Prediction error

1857

In a resting animal, midbrain dopamineneurons show a slow, steady (“tonic”) rateof firing; certain meaningful stimuli pro-voke brief, abrupt (“phasic”) changes indischarge. In previous work, Schultz andco-workers (2, 3) recorded the activity ofsingle dopamine neurons in awake mon-keys with microelectrodes. When themonkey received an unexpected reward,such as a drop of juice, most dopaminecells responded with a burst of firing.However, if the monkey learned that astimulus, such as a particular pattern on acomputer monitor, always preceded deliv-ery of the reward, the dopamine neuronsno longer responded to the reward butfired instead in response to the predictive(“conditioned”) stimulus. Omission of apredicted reward caused slowing or cessa-tion of dopamine firing around the timethat the reward was expected.

The phasic responses of midbraindopamine neurons resemble a key signalin computer models based on animallearning (4–7). Through adjustment ofconnection weights in a neural network(“reinforcement”), these models are ableto predict the achievement of a goal state(“reward”) and result in optimization ofactions. The incremental improvement inpredictions is driven by a “prediction er-ror”: the difference between expected andexperienced rewards. The new findings ofFiorillo et al. both strengthen and chal-lenge the reinforcement-learning notion of

midbrain dopamine neuronal activity whileraising many fascinating new questions.

The novelty of the Fiorillo et al. studylies in their systematic variation of the pro-portion of conditioned stimuli that werefollowed by a reward. The conditionedstimulus associated with each reward prob-ability (0.0, 0.25, 0.50, 0.75, 1.0) consistedof a unique visual pattern displayed on acomputer monitor for 2 seconds (see thefigure). Delivery of the reward, a drop ofsyrup, coincided with the offset of the con-ditioned stimulus. The dopamine neuronsresponded to the conditioned stimuli withphasic increases in firing that correlatedpositively with reward probability. In con-trast, responses to reward delivery showeda strong negative correlation with rewardprobability. On trials when an expected re-ward was omitted, the firing of dopamineneurons tended to decrease at the time ofpotential reward delivery, and the magni-tude of this dip tended to increase with theprobability of a reward. The systematicvariation in the strength of the phasic re-sponses to conditioned stimuli and to re-ward delivery or omission supports and ex-tends previous findings obtained at the twoextreme probabilities (0.0 and 1.0): Thehigher the likelihood of reward, the

stronger the firing to the conditioned stim-ulus, the larger the decrease in firing to re-ward omission, and the weaker the firing toreward delivery.

Their most provocative results concernthe activity of the dopamine cells beforethe time of potential reward delivery. Inprevious work carried out at the two ex-treme probabilities, the firing rate was sta-ble. By exploring intermediate probabili-ties, Fiorillo et al. reveal a striking new pat-tern: The activity of dopamine neurons in-creased before the potential delivery of anuncertain reward. In contrast to the briefupswings in firing triggered by reward-predicting stimuli and unexpected re-wards, the population firing rate rosesteadily throughout most of the 2-secondpresentation of the conditioned stimuluswhen the probability of reward was 0.5, at-taining a higher rate than when the rewardprobability was 0.25 or 0.75.

The authors propose that the sustainedfiring preceding the time of potential re-ward delivery tracked the uncertainty ofthe reward. The onset of the conditionedstimuli signaling probabilities of either 0.0or 1.0 provided the monkey with definitiveinformation as to whether the rewardwould be delivered; if the meaning of thestimuli had been fully learned, then uncer-tainty about reward occurrence followingstimulus onset would be zero. In contrast, areward was equally likely to be delivered oromitted when the probability was 0.5, andthe monkey should have been maximallyuncertain about reward occurrence. Whenthe reward probability was 0.25 or 0.75,uncertainty was intermediate; if the mon-key had bet on the occurrence of the rewardfollowing stimulus onset, it could havewon, on average, three trials out of everyfour.

Regarding reinforcement learning mod-els, the phasic response of the dopamineneurons encodes a prediction error that isused as a “teaching signal” to improve fu-ture predictions. The incremental adjust-ment of the weights causes the teachingsignal to move backward in time toward theonset of the earliest stimulus that reliablypredicts the occurrence of a reward (and/orits potential time of delivery). How the sus-tained response preceding potential rewarddelivery could be incorporated into suchmodels is hard to see. If the dopamine sig-nal serves as the teacher, and the sustainedcomponent is not filtered out, how couldthe sustained component remain stationaryin time and amplitude over many trials?This problem is a knotty one because thesustained and phasic signals do not appearto be carried by independent populations ofneurons. Thus, postsynaptic elements arelikely to register the combined impact of

CS

Reward

P U

1.0

0.5

0.0

0.0

1.0

0.0

Anticipating a reward. Electrophysiological re-sponses of single midbrain dopamine neurons(yellow) were recorded while monkeys viewed acomputer monitor. Unique visual stimuli wereassociated with different probabilities of a re-ward (a drop of syrup). Rewards were presentedwith different probabilities (P) when the condi-tioned stimulus was switched off (CS offset).Theuncertainty (U) of the reward varied as an in-verted U-shaped function of probability. When P= 0 or P = 1, the monkey is certain that rewarddelivery will or will not accompany CS offset. Incontrast, when P = 0.5, the onset of the CS pro-vides no information about whether reward willor will not occur, but it does predict the potentialtime of reward delivery. The interval betweensuccessive CSs varied unpredictably (not shown),and thus the onset of the CS is the earliest reli-able predictor of the occurrence and/or the po-tential time of reward delivery. Once the mean-ing of the stimuli has been learned, the popula-tion of dopamine neurons responds to CS onsetwith a brief increase in activity when P = 1.0, butreceipt of the expected reward does not provokea strong change in firing. When P = 0, CS onsetproduced little response; if, however, the investi-gator violated expectation by delivering a re-ward, the dopamine cells responded with a brief,vigorous increase in firing.When P = 0.5 (and un-certainty about reward occurrence is maximal), aslow, steady increase in firing is seen prior to thetime of potential reward delivery.C

RED

IT:K

ATH

ARI

NE

SUTL

IFF/

SCIE

NC

E

www.sciencemag.org SCIENCE VOL 299 21 MARCH 2003

P E R S P E C T I V E S

–R

S R

+R

Schultz et al., Science, 1997

Addiction

Planned decision Automatic decision/Habit

Context

Initiation AddictionRepeated Consumption

« Pleasure » Automatisation/Habit

dopamine

Nicotine Gambling ChocolateCocaine Morphine

Addiction

Compulsion/habit

Mesolimbic dopamine ↑

Addiction

Animal models to study addiction

Memory trace in rodents600

400

200

0

seconds

PRE TEST

**A B A B

Excited by “blue light”

Ventral tegmental area (VTA)

GABA

DA

Nicotine Gambling ChocolateCocaine Morphine

Addiction

Compulsion/habit

Hijacking of learning and memory

Mesolimbic dopamine ↑

Addiction

Synaptic plasticity

And here is what we have done over the last few years to experimentally test this hypothesis.

Mesocorticolimbic system

Excitatory transmission

Rat age 10-18 daysThickness 300µm

Mouse P20-P75Thickness 300µm

32-340 C

Slice preparation

Slice preparation

• pitx3-GFP+/–

• Meng Li, London

VTA

The synapse

Boutonsynaptique

Dendrite

Récepteur Glutamate

0 mV

10 ms

5 mV

Increase in neurotransmitter

Glutamate

0 mV

10 ms

5 mV

Récepteur

Increase in number of Receptors

Glutamate

0 mV

10 ms

5 mV

Récepteur

Synaptic plasticity

Cocaine:increase in AMPA/NMDA

Cocaine:increase in AMPA/NMDA

Cocaine:increase in AMPA/NMDA

Cocaine:increase in AMPA/NMDA

AMPARs

Na2+% Ca2+%

Common synaptic adaptation

stresssaline cocainemorphine nicotine

-100

-50

50

100

-80 -40 40

n = 8 -100

-50

50

100

-80 -40 40

n = 8 -100

-50

50

100

-80 -40 40

n = 9 -100

-50

50

100

-80 -40 40

n = 10 -100

-50

50

100

-80 -40 40

n = 9

pA

mV

in d-APV

Common synaptic adaptation

stresssaline cocainemorphine nicotine

-100

-50

50

100

-80 -40 40

n = 8 -100

-50

50

100

-80 -40 40

n = 8 -100

-50

50

100

-80 -40 40

n = 9 -100

-50

50

100

-80 -40 40

n = 10 -100

-50

50

100

-80 -40 40

n = 9

pA

mV

Class IIIClass I Class II

Bellone et al., 2006; Brown et al., 2010

AMPAR subunit composition