Yale Psychiatry Grand Rounds: October 15, 2021
October 15, 2021Information
Flynn Lecture: "Dynamic Reward Signaling in Ventral Pallidum"
Patricia Janak, PhD, Bloomberg Distinguished Professor, Department of Psychological and Brain Sciences, Department of Neuroscience, Johns Hopkins School of Medicine
ID7045
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- 00:00But we're still going to have
- 00:02people joining for awhile,
- 00:03but I I would like to make sure that I leave.
- 00:05There's not much time as possible
- 00:07for Doctor Janik to to speak,
- 00:09so I will begin our introduction.
- 00:12Those of you who heard The Chieftains,
- 00:15this music was in honor of
- 00:19Doctor Flynn's Irish heritage,
- 00:21although I do think The Chieftains
- 00:23might be Scottish, as Jane mentioned,
- 00:25but the IT was still lively and Celtic, and.
- 00:31As as you'll hear Doctor John Patrick Flynn,
- 00:36who for whom this lecture is name is named,
- 00:40was a member of the L faculty from 1954
- 00:43until his retirement in July 1979,
- 00:46and he was really an extraordinary
- 00:48person who had quite a remarkable life.
- 00:51And so I'm going to take a
- 00:52little time to tell you about it.
- 00:54We have his daughter,
- 00:56Sarah Flynn, with us here today.
- 00:58Thank you for coming, Sarah, and.
- 00:59She was the one who helped me
- 01:02gather the information that I'm
- 01:04going to be sharing with you today.
- 01:07So first of all,
- 01:08I I knew Dr.
- 01:09Finn Flynn's work because of his focus
- 01:12on the neural basis of aggressive
- 01:14behavior and he's recognized as
- 01:16a pioneer in neuroscience and in
- 01:18general for his contribution to
- 01:20understanding the function of the
- 01:23hippocampus in the hypothalamus.
- 01:25He also served from 1968 to 1978
- 01:28as director of the Abraham Ribicoff
- 01:30Research facilities at the Connecticut
- 01:32Mental Connecticut Mental Health Center,
- 01:34which is where most of our basic or
- 01:37a large chunk of our basic science
- 01:40labs remain right in in proximity
- 01:43to our clinical research facilities,
- 01:46which was something that was essential
- 01:49for establishing the translational
- 01:51and collaborative nature of the
- 01:53department and since 1982.
- 01:54We've had a lecture held in his
- 01:57honor recognizing his quote pivotal
- 01:59role in establishing the central
- 02:01importance of basic neuroscience
- 02:03research as their frontier for
- 02:05clinical psychiatric studies.
- 02:07And that's a tradition that we
- 02:09honor today with our speaker,
- 02:10doctor Patricia Janik.
- 02:12So here's now why we had Celtic music.
- 02:15Doctor Flynn was born in Superior, WI.
- 02:17The of an Irish immigrant mother and
- 02:20a first generation Irish American
- 02:22father who worked as a railroad
- 02:25switchman and he studied for the priesthood.
- 02:28He was ordained in Rome and then he
- 02:30returned to the United States in 1938
- 02:33to Loyola University and when his
- 02:35superiors there decided that they
- 02:37needed someone to teach psychology,
- 02:39he volunteered to study it.
- 02:41And then he went out.
- 02:43To find the best teacher so he
- 02:45could actually teach his students.
- 02:47This led him to Columbia University
- 02:49and there he studied psychology
- 02:51and he remained in the priesthood.
- 02:53But throughout this time he
- 02:55was examining his conscience.
- 02:57And he ultimately resigned from the
- 02:59priesthood and left the church in 1944,
- 03:02having received his PhD in
- 03:05experimental psychology in 1943.
- 03:07In 1944, Dr Flynn went to work at Harvard,
- 03:11where he did war work related
- 03:13to aviation and audition,
- 03:15and in late 1945 he married
- 03:18a holder Isma Garvey,
- 03:20and she was someone who I met
- 03:22when I first
- 03:23came to to Yale,
- 03:25and she would come with Sarah.
- 03:27To listen to the lecture and she was also
- 03:30an incredible and remarkable individual.
- 03:33She was a psychologist and in 1946
- 03:36and she was here actually as a member
- 03:39of the Department of Psychiatry,
- 03:42first appointed in 1962 as a research
- 03:45assistant and then serving on the
- 03:47planning project for the Connecticut
- 03:49Mental Health Center and finally.
- 03:52Working closely with Doctor Boris Astrachan,
- 03:55who was instrumental in founding the
- 03:58the Connecticut Mental Health Center,
- 04:00she she retired as a valued member
- 04:02of the medical school faculty.
- 04:05So in 1946,
- 04:08Doctor Flynn was appointed head
- 04:10of the psychology and Statistics
- 04:12Division at the Naval Medical
- 04:14Research Institute in Bethesda,
- 04:15and there he began his work
- 04:18in physiological psychology.
- 04:19And here's where the story gets even
- 04:22more interesting despite excellent
- 04:24performance reviews and general
- 04:26acclaim by his fellow scientists.
- 04:28Doctor Flynn was fired in 1953,
- 04:31and he was deemed a risk to
- 04:33national security for his quote.
- 04:35Close and continuing association
- 04:37End Quote with his wife,
- 04:40whose name had been named before the house.
- 04:42A committee on UN American activities
- 04:44during the McCarthy ERA era.
- 04:47Because an error is a correct
- 04:51Freudian slip because of of whole,
- 04:54this political activities in the 1930s
- 04:57and early 40s and doctor Flynn was
- 05:00offered the chance to keep his job if
- 05:03he divorced and he of course declined.
- 05:06Over the next six months or so,
- 05:08he received offers of employment from
- 05:10colleagues across the country at 13
- 05:12universities, and he told his daughter,
- 05:15Sarah Flynn,
- 05:15that each time his name reached
- 05:18the provost's office,
- 05:19the colleague was informed that
- 05:22the university could not hire Dr.
- 05:24Flynn, and in September 1954,
- 05:27Yale hired him to work with
- 05:30Doctor Paul McLean,
- 05:31who then held a joint appointment
- 05:33in Physiology and Psychiatry and
- 05:34who was studying the limbic system.
- 05:36So yell was able to benefit from
- 05:40his neuroscience area addition
- 05:43in the face of strong headwinds.
- 05:46Come upon a learning of so.
- 05:52Doctor Flynn then became a member
- 05:54of this of the department and
- 05:56worked until he retired in 79 on the
- 06:00physiological basis of aggression
- 06:03and he really made a a an incredible
- 06:07mark on the department,
- 06:09and upon learning of his death,
- 06:11Fritz Redlich,
- 06:12whose chair of the Department of Psychiatry,
- 06:13wrote to holder Flynn at John
- 06:15was all I ever wanted to be.
- 06:18A fine scientist and teacher,
- 06:20and most of all,
- 06:21an extraordinary human being.
- 06:22I've always admired his courage
- 06:24and integrity, two virtues,
- 06:26high value above anything else,
- 06:29and similar sentiments were
- 06:30expressed by other colleagues both
- 06:32at Yale and around the world.
- 06:33And the last thing I want to say before
- 06:36I I move on is also about Sarah Flint.
- 06:40Generate generosity to the department.
- 06:42So in April 2005,
- 06:45Sarah donated Dr.
- 06:47Flynn's most prized possession,
- 06:48the three volume set of romantica
- 06:51halls fixed Judah,
- 06:52their system and nervioso de Lumbre.
- 06:54Elizabeth brought us.
- 06:56In the original Spanish to the
- 06:59Yale Medical Historical Library
- 07:00and inside the first
- 07:02volume is an inscription written
- 07:04by Cahal in 1910, which reads in
- 07:08translation because of the brain.
- 07:10Man is the king of Creation,
- 07:12and to clarify the structure of the
- 07:14brain is to understand why that figure
- 07:16is at the head of the animal Kingdom
- 07:18and how civilization was created.
- 07:20A sign of human superiority
- 07:22to the rest of the beings.
- 07:23This may not actually translate
- 07:25so well to the current.
- 07:27Sarah, my original idea was that I would
- 07:29not be able to take a sure step in
- 07:31the study of Physiology and pathology
- 07:33of the nervous system without knowing
- 07:35the cerebral machine with precision,
- 07:37and that the mysteries of the science
- 07:39of the spirit will only be clarified
- 07:42when all the unknowns relative to
- 07:44the chemistry of the fine structures
- 07:46of the nerve cell are cleared up.
- 07:49And luckily, of course,
- 07:50we're completely done with that,
- 07:51and Doctor Janik will will give the
- 07:54the the heading to that to that quote.
- 07:58And just to finish up,
- 08:00Doctor Flynn always took delight in
- 08:02telling the story that he acquired
- 08:04these books for $10 at a used bookstore
- 08:07in New York sometime during the 1940s.
- 08:11So I I will finish there and I I hope
- 08:16that you will now join me in welcoming.
- 08:20Sorry I will get through.
- 08:22These are 2021 lecturer in the
- 08:26Flynn Memorial Lecture series,
- 08:28Doctor Patricia Janik and so Dr Janik
- 08:33is the A Bloomberg distinguished
- 08:35professor at Johns Hopkins University
- 08:37with appointments in the Department
- 08:39of Psychological and Brain Sciences.
- 08:41And the Krieger School of Arts
- 08:43and Sciences and the Department
- 08:45of Neuroscience in the School of
- 08:47Medicine and Doctor Janik Studies.
- 08:50Neural processes of reward learning.
- 08:52And you'll hear a lot about that today.
- 08:54She's especially interested in learning
- 08:56mechanisms underlying addiction,
- 08:57which is an area where this department
- 09:01certainly has extremely strong interest.
- 09:04She earned her pH.
- 09:05D.
- 09:05From the University of California,
- 09:06Berkeley,
- 09:07and then she conducted postdoctoral research
- 09:09at Wake Forest and at the National Institute.
- 09:11And drug abuse and come in from 1999 to 2014,
- 09:15which is the period when I first
- 09:18came to know her.
- 09:19She was faculty at the University
- 09:21of California at San Francisco,
- 09:22where she was the Howard J Weinberger,
- 09:25MD endowed Chair and addiction
- 09:27research at UCSF.
- 09:29She's a pioneer in the
- 09:31identification of neural circuits,
- 09:33underlying alcohol and drug seeking,
- 09:35and her work is really spanned
- 09:38levels of investigation from the
- 09:40molecular and synaptic plasticity.
- 09:42All the way to in vivo mechanisms
- 09:44that are relevant to complex models
- 09:45that are relevant to addiction,
- 09:47and this includes alcohol and
- 09:49drug seeking relapse,
- 09:51habit learning,
- 09:52extinction learning,
- 09:53and she's used.
- 09:54Everything from electrophysiological
- 09:56approaches to neuronal
- 09:57imaging and optogenetics,
- 09:59which I'm sure you'll hear about today,
- 10:01and if you're looking for a
- 10:02resource to understand the neural
- 10:04circuitry relevant to addiction,
- 10:06you need to read her 2021 review
- 10:08on consolidating the circuit
- 10:09model for addiction that she
- 10:11wrote with Christian luescher.
- 10:13And that appears in the annual
- 10:15review of neuroscience.
- 10:17And I first met Doctor Genich through her
- 10:18roles at the Society for Neuroscience.
- 10:20She's she's done a lot for the
- 10:22society she served as reviewing editor
- 10:24at the Journal of Neuroscience.
- 10:26She's been chair of the program committee,
- 10:28probably for way too long, given kovid,
- 10:30and she's incoming secretary of the society.
- 10:34She's also served on the Program Committee
- 10:37for the Research Society and Alcoholism,
- 10:39and She's been Co Chair and Chair
- 10:42of the Catecholamines and the
- 10:44Alcohol Gordon Conferences,
- 10:45so you can see that the
- 10:47influence of her work.
- 10:48In the field is extremely broad and
- 10:51I just want to close by saying that
- 10:53Doctor Janik is much more than her CV.
- 10:55She's been a mentor to leaders in the
- 10:57field who study the neurobiology of
- 10:59addiction and other behaviors that
- 11:01are relevant to psychiatric illness.
- 11:03When my own lab was trying to figure
- 11:06out issues related to experimental
- 11:08design for in vivo calcium imaging,
- 11:10Katie told me that the most important
- 11:12thing she ever learned in her scientific
- 11:14career was from Doctor Janik.
- 11:16And that is how to start with.
- 11:18Robust experimental design that gives
- 11:19you the adequate power to that's
- 11:22essential to get robust Physiology data,
- 11:24and we've certainly taken this
- 11:25to heart as well.
- 11:26So as you can tell,
- 11:28Dr Janik is a role model for many
- 11:31and she is the ideal person to give
- 11:34the Flynn Memorial lecture this year.
- 11:37So please let's welcome Dr.
- 11:39Janik and I will stop sharing and
- 11:42allow her to share her data with you.
- 11:47Thank you so much for that.
- 11:49It was such an an amazing introduction
- 11:52and I really enjoyed learning about.
- 11:56Now, Doctor Flynn and can
- 11:58you see my screen? Yes.
- 12:02I have to say
- 12:02what it, what a deep honor it is to be
- 12:04invited to give this particular lecture.
- 12:07I it was such an interesting history
- 12:09and I really hope that you find that
- 12:11the kind of work that I talk about
- 12:14today resonates with the kinds of
- 12:15things that he was interested in,
- 12:17so that you can see that it's a good fit.
- 12:19And I I want to thank you, Marina for
- 12:21the invitation and this opportunity.
- 12:23And thank you for such an A.
- 12:27Humbling introduction that that was really,
- 12:29really so nice and I'll try my
- 12:31best to live up to everything that
- 12:33has been said in this talk.
- 12:35So, uh, welcome to everybody and I'm
- 12:38sorry I'm not meeting you today in person,
- 12:41but I'm so happy to talk to you even
- 12:43though we're over zoom and if any
- 12:45questions come up during the talk.
- 12:47I'm sure people will help me to try to
- 12:49answer those since I don't think I'll
- 12:51be able to see the chat very well.
- 12:54OK, so I'd like to tell you about
- 12:57our experiments today.
- 12:58Looking at reward processing
- 13:00in the nervous system,
- 13:02specifically focusing on the
- 13:04area called the ventral pallidum,
- 13:07and I want to first tell you a
- 13:08little bit about the motivation
- 13:10for us in our lab in looking at
- 13:13reward seeking behavior models and
- 13:16the underlying neural circuitry and
- 13:18what we are interested in broadly,
- 13:20is what the processes are.
- 13:23The determined reward seeking behavior.
- 13:25Whether that reward is a food reward,
- 13:27something that our nervous system
- 13:29evolved to help us discover and ingest,
- 13:32or whether it's a drug reward,
- 13:34something that it's very important
- 13:35for us to understand as we think
- 13:37about how we can help individuals
- 13:39with substance use disorders,
- 13:41and we conceive of these processes
- 13:43through the lens of psychology.
- 13:46And there really are three interrelated
- 13:49psychological processes that
- 13:50determine at any one moment whether
- 13:52an agent will seek a given reward.
- 13:54And so first you have the.
- 13:55Real time decision.
- 13:56Will you decide to reach out
- 13:59your arm and grab that hamburger?
- 14:01Will the attic decide to call up
- 14:03the dealer to try to get that next
- 14:05fix so you have your real time
- 14:07decision that's impacted critically
- 14:09by your current motivational state?
- 14:12Whether you're hungry,
- 14:13whether you're thirsty,
- 14:14whether you are a person with an
- 14:16abuse disorder, whose craving drug,
- 14:18your decision is is necessarily filtered
- 14:21through your motivational state.
- 14:23And both of these critically depend
- 14:26on past experience or learning.
- 14:28So your past evaluation of the
- 14:31subjective effects of the rewards
- 14:33that you've experienced and you're
- 14:35learning about the conditions under
- 14:37which you obtain those rewards is
- 14:39critical for you in the future.
- 14:41When you're making that decision
- 14:43to get that reward so you know the
- 14:45actions to take or not take,
- 14:47and you understand the meaning of
- 14:49the stimuli in the environment.
- 14:51So we have these three interacting processes.
- 14:53And we're interested in the neural
- 14:55circuits that underlie them,
- 14:56so we can understand decision
- 14:59making both in normal
- 15:01conditions like feeding and also our
- 15:03end goal is to better understand this
- 15:06circuit so we can help explain decisions
- 15:09made by people who have substance use
- 15:12disorders or alcohol use disorders,
- 15:14because these same processes of
- 15:16course are occurring when one makes
- 15:19the decision to continue taking it.
- 15:21Drink for example, or to take another.
- 15:23Hit of that drug.
- 15:26So through many, many decades of work
- 15:29in a nonhuman animals and in humans.
- 15:33We've discovered as a field.
- 15:36A group of interconnected circuits
- 15:38that are called the canonical reward
- 15:40seeking circuit and of course.
- 15:42This circuit as many of you know
- 15:45overlaps extensively with the with the
- 15:47limbic system so something that doctor,
- 15:49Flynn would have been very well acquainted
- 15:51with and a circuit with in which he
- 15:54would have spent much of his time.
- 15:56In his research efforts.
- 15:58I'm going to focus on a subset of
- 16:01regions within this circuit here
- 16:03depicted in this cartoon schematic
- 16:05from the rodent brain where we
- 16:07see the nucleus incumbents.
- 16:08The most ventral aspect of this striatum,
- 16:11where we see its output,
- 16:13then one of its outputs,
- 16:15the ventral pallidum,
- 16:16which is analogous to globis pallidus
- 16:18in more dorsal striatal circuits and
- 16:21dopaminergic input to these regions
- 16:23from the VTA and other areas that
- 16:25we know and love like the amygdala.
- 16:27And so my lab is very interested in.
- 16:29How these reward circuits evaluate
- 16:33reward when it's being experienced?
- 16:36And then how that current evaluation
- 16:38can impact how the animals learn
- 16:41about what just happened so that it
- 16:44can impact their future behavior?
- 16:46So how are rewards processed in this circuit?
- 16:49So we're going to focus today specifically
- 16:51on trying to understand how the ventral
- 16:53pallidum contributes to that process,
- 16:55and it's much more interesting than
- 16:57perhaps we once thought few decades ago,
- 17:00the ventral pallidum historically
- 17:02has been considered somewhat of
- 17:04a way station or pass through.
- 17:06For information from this striedl region,
- 17:09the accompagnes,
- 17:10but instead increasingly,
- 17:11we're understanding that really important
- 17:14integrative processing is happening
- 17:16at the level of the ventral pallidum
- 17:19that impacts reward seeking behavior.
- 17:21So I'm going to focus in on some
- 17:23experiments conducted in the lab that
- 17:25I hope can can illuminate the function
- 17:27of the ventral pallidum for us,
- 17:29and when you wonder what a brain region does,
- 17:32of course one of the most traditional
- 17:34ways to look is to get rid of that brain.
- 17:36Region so decades ago,
- 17:38lesions of the ventral pallidum
- 17:40were shown to decrease intake of
- 17:42drugs of abuse in animal models.
- 17:45So decrease opiate self administration,
- 17:47for example.
- 17:49So that tells us this this area is very
- 17:51important for reward seeking behavior,
- 17:53but to figure out how it's very
- 17:55instructive to go in and use
- 17:58electrophysiology to record the neural
- 18:00activity during the behavior so you
- 18:02can see what the neurons care about,
- 18:04and so a number of labs have used this
- 18:06approach and I'd like to tell you about.
- 18:08Some of the data that has
- 18:10already emerged that set
- 18:11up our thinking for our experiments.
- 18:13So these are our data obtained from in vivo
- 18:18electrophysiological recordings in rats.
- 18:20So electrodes are implanted
- 18:21into the ventral pallidum,
- 18:22and we're measuring extracellularly.
- 18:24The spike activity of nearby neurons,
- 18:26and what Jocelyn,
- 18:28Richard working with Howard Fields found,
- 18:30is that neurons in the ventral pallidum
- 18:33care about cues that tell the animal
- 18:36it has an opportunity to get reward,
- 18:38and so here we see an average
- 18:40increase in activity when the animal.
- 18:42Here's a Q, but.
- 18:43The increase in activity is much bigger
- 18:45when the animals actually motivated
- 18:47to press a lever to get the reward,
- 18:49so the the larger signal is from trials
- 18:52when animals press the lever to get reward
- 18:54and the smaller signals when they fail to.
- 18:56So motivation already is coming into
- 18:58play and modulating the way the ventral
- 19:01pallidal neurons respond to cues.
- 19:04The valence of a reward also modulates
- 19:06responses in the ventral pallidum.
- 19:08So, as you might predict,
- 19:10given the data I told you about lesions,
- 19:12the ventral pallidum cares
- 19:14about the nature of the reward.
- 19:16So in this example from Kendall
- 19:17at all this this classic paper,
- 19:20we see examples from 1 neuron
- 19:22and its response to two QS.
- 19:24One is a Q that predicts something good,
- 19:26sucrose solution and one is a
- 19:28cue that predicts something the
- 19:30animal doesn't like a salty taste.
- 19:31We see the neuron has a big
- 19:33increase in firing to the.
- 19:35Q.
- 19:35Predicting something good
- 19:36and not to the salt Q.
- 19:39But if we use pharmacology to
- 19:41deplete the subjects of salt,
- 19:43so we'd make them desire salt and want salt.
- 19:46We see suddenly that this neuron
- 19:48now responds with an increase
- 19:50in activity to the salt Q.
- 19:52Reminiscent of the response to the sucrose Q.
- 19:54So this is a clue that the Q responses
- 19:57responses in the VP are sensitive to
- 20:00the valence of the reward the animal
- 20:02is going to get, so it cares about.
- 20:05Motivational state it cares
- 20:07about the valence of the reward.
- 20:09And interestingly,
- 20:10in this work from bullies Lab,
- 20:12it also cares about what's
- 20:14actually happening in real time.
- 20:16So here we see an example of
- 20:18data from a paper recording from
- 20:20neurons in the ventral pallidum
- 20:22in subjects that receive the queue
- 20:25that predicts sucrose reward.
- 20:26The neurons respond to the queue,
- 20:28and they respond to the reward.
- 20:30But if on some trials you omit reward
- 20:32that you see that there is a decrease.
- 20:35In firing by these neurons,
- 20:37this tells us that neurons have
- 20:39an expectation of the reward that
- 20:42they should receive after the Q.
- 20:44This is reminiscent of a negative
- 20:46reward prediction error signal that
- 20:48many of you may be familiar with from
- 20:50thinking about how dopamine neurons
- 20:52respond when expected reward fails to arrive.
- 20:55So together these give us important
- 20:58clues that neurons in the ventral
- 21:00pallidum respond to cues.
- 21:02They respond to reward,
- 21:03and they do so in interesting manners.
- 21:05They care about the motivational state.
- 21:07They care about the valence of the reward,
- 21:09how much the subject likes the reward,
- 21:11and they have some sort of
- 21:14expectation information.
- 21:15They care about what's
- 21:16happening in real time.
- 21:17If the reward arrives or not.
- 21:19So these and many other lovely
- 21:21studies set the stage for the kinds
- 21:24of questions that David Ottenheimer,
- 21:26a graduate student in the lab,
- 21:28wanted to ask when he wondered about
- 21:30the details of how the outcomes
- 21:33themselves are processed by the neurons.
- 21:36In ventral pallidum.
- 21:36So David was a graduate student in my lab.
- 21:39He's now a postdoc in the
- 21:41Steinmetz and Stuber Labs,
- 21:42and he was aided through all of
- 21:45this with by Doctor Joslin Richard,
- 21:47when she was a senior scientist in the lab.
- 21:50She was our resident ventral pallidum expert.
- 21:52Doctor Richard now runs her own
- 21:54lab at the University of Minnesota,
- 21:57so together they designed a
- 21:59series of studies to allow them
- 22:01to understand better how ventral
- 22:04pallidum neurons encode natural.
- 22:06Word outcomes and so that David was
- 22:09interested in doing these studies
- 22:12in the setting of multiple rewards.
- 22:15Because eventually we'd like to
- 22:17understand how agents make choices
- 22:19among rewards because we'd like to apply
- 22:22this in the future to drug addiction.
- 22:24How to agents choose drugs or other rewards.
- 22:28So David began with very simple.
- 22:31Experimental designs where he could
- 22:33look at the activity of ventral
- 22:35pallidal neurons when rats were
- 22:37receiving more than one reward.
- 22:39So in this very initial simple design,
- 22:41he implanted electrodes into
- 22:43the ventral pallidum of rats,
- 22:45recorded extracellular spike activity.
- 22:47These are waveforms of example
- 22:49neurons that he recorded,
- 22:51and here's again our cartoon
- 22:52of the ventral pallidum.
- 22:53So the electrode tips are residing in the VP,
- 22:56and he exposed subjects to
- 22:59two different rewards liquid.
- 23:02Sucrose and maltodextrin.
- 23:03These are both carbohydrates,
- 23:05calorically equivalent,
- 23:06and that we know rats like they will
- 23:09drink then avidly and he exposed them
- 23:12to these rewards in recording sessions,
- 23:15and the rewards were delivered randomly
- 23:17so the animal didn't know which
- 23:19reward was coming on any given trial.
- 23:22Specifically,
- 23:22he played the queue so white noise Q,
- 23:26and when the animal heard the cue,
- 23:27the animal knew it could go to the port
- 23:29when it put its snout in the reward.
- 23:32Port Reward was delivered,
- 23:33and then there's an Inter trial
- 23:35interval and that occurs again
- 23:37and so the the reward cannot be
- 23:39predicted by the queue.
- 23:40The animal has to actually wait till
- 23:43the reward squirt it out before
- 23:45it knows what it's getting so we
- 23:47can compare the neural response
- 23:48to these two rewards.
- 23:50There's an interesting feature
- 23:51about these two rewards and that is,
- 23:53although rats love both of them,
- 23:56if you give them a full bottle of
- 23:58one or the other on their homepage,
- 23:59they'll drink it all up.
- 24:01If you give them two bottles.
- 24:02One with sucrose,
- 24:03one with maltodextrin at the same time,
- 24:06most rats prefer the sucrose,
- 24:08and that's what I'm showing here
- 24:10in this behavioral data figure.
- 24:12This shows the preference subjects
- 24:14have for sucrose over
- 24:16maltodextrin when tested in the
- 24:17home cage when they just
- 24:19have big bottles of both,
- 24:20they'll drink more of the
- 24:22sucrose than multidex turn.
- 24:24However, in this behavioral session
- 24:25where we're giving A Q and then
- 24:28squirting out maltodextrin or sucrose
- 24:30and they have to drink it in order
- 24:32to have the next trial happen,
- 24:34we see that the licking behavior
- 24:36when they're consuming the different
- 24:39rewards is almost identical.
- 24:41So we're left with a nice,
- 24:42very simple behavioral model where their
- 24:44preference for the two rewards is different.
- 24:47We know that based on these
- 24:49long term drinking studies,
- 24:51but their motor behavior during this
- 24:53particular very simple task is very similar,
- 24:56so that gives us a nice way to
- 24:57see what the signal related to the
- 24:59preference might be when we're
- 25:01basically making sure that the motor
- 25:03behavior is not that different,
- 25:04because that could motor behavior
- 25:06could be an explanation for
- 25:08some differences that we see.
- 25:10So in the face.
- 25:11In this very simple behavior,
- 25:13what David found when he recorded from
- 25:15many neurons in the ventral pallidum,
- 25:17many individual neurons is that there's
- 25:19a big difference in the way the neuron
- 25:22signal which reward they received.
- 25:24So here I'm showing the average activity
- 25:27of 205 neurons that were sensitive to
- 25:30reward based on statistical analysis.
- 25:33If we divide the trials into those in
- 25:35which the animal receives sucrose and orange,
- 25:38or maltodextrin in this pink purple color.
- 25:41We see an average very large increase in
- 25:43activity when the animals are drinking.
- 25:45The sucrose 0 is the time that rewards
- 25:48delivered the first few seconds,
- 25:50first three to four seconds is when
- 25:52they're actually lapping it up.
- 25:53We see a much lower response by the
- 25:56population when maltodextrin is received.
- 25:59These heat maps here show you the
- 26:01activity of the individual neurons
- 26:02that make up these averages,
- 26:04so again we have the same time course
- 26:07and each row is the color coded map of
- 26:09the spike intensity from that neuron
- 26:12arranged by most intense to less intense.
- 26:14And you can see here by I many,
- 26:16many neurons are showing an
- 26:18increase at this exact time,
- 26:19and it's much less present and sometimes
- 26:22even more of a decrease for maltodextrin.
- 26:25So the populations in the ventral pallidum
- 26:27encode these two rewards differently.
- 26:29Although the drinking behavior similar the
- 26:31preference these subjects have is different,
- 26:34and that may be what is we're seeing here.
- 26:37Alternatively,
- 26:38you might propose will.
- 26:39Sucrose is a very important natural sugar.
- 26:42Maybe neurons in the brain are set
- 26:45up already to fire in a very specific
- 26:48way to sucrose as as it taste it.
- 26:51So it could be that these responses
- 26:53are fixed and that they really
- 26:55depend on the rewards.
- 26:57So David tried to think of a way to to
- 26:59examine that so to do that he repeated
- 27:02the same behavioral procedure but he
- 27:05swapped water for sucrose. So now.
- 27:07He's going to give the animals up.
- 27:10Interleaved sessions,
- 27:11when they receive sucrose or water after
- 27:14the queue and you could see their behavior.
- 27:17In fact, they're licking behavior
- 27:18is different from water because
- 27:20they're not water restricted.
- 27:22So they don't really want water very much
- 27:25and what he saw neurally is a switch in
- 27:29the way neurons encoded maltodextrin.
- 27:31So remember the activity for Maltodextrin
- 27:33was much lower than for sucrose when
- 27:36those were the two rewards being compared.
- 27:38But now when we are comparing maltodextrin
- 27:41and water as the animals taste each one,
- 27:43we see a relative increase in the
- 27:46response from maltodextrin and a
- 27:47decrease in the response for water.
- 27:49And you could see that very
- 27:50clearly in these heat maps.
- 27:52Here's a sucrose here's water,
- 27:55but most interesting focus on
- 27:57maltodextrin relatively low activity
- 27:59across the population now very.
- 28:01High activity across the population.
- 28:04So this readout is not fixed based
- 28:06on the the actual chemical nature of
- 28:09the taste it so instead it it seems
- 28:12as if perhaps it relates more to the
- 28:14animal's current preference for example.
- 28:17And we can see very exactly similar
- 28:20results if we run a behavioral
- 28:22session with all three liquid's
- 28:24randomly presented after the Q,
- 28:272 hour rats and we see a much higher
- 28:30average neural response to sucrose medium
- 28:33for maltodextrin and big decrease for water.
- 28:36So so there there's a ranking in
- 28:39the neural activity that fits what
- 28:41we might think of as the ranking of
- 28:44the animal subjective preference.
- 28:46So that's one way we could.
- 28:48We could wonder about what the
- 28:49signal means for the animal.
- 28:51Is this just a readout of the animal's
- 28:54current subjective preference?
- 28:55Another idea that David had when
- 28:57looking at this is that this signal
- 29:00also could be a readout of a
- 29:02difference from the animals,
- 29:04expectation of reward value.
- 29:06So each time the animal,
- 29:08here's the queue and is going
- 29:10to the port to get reward,
- 29:12it doesn't know which rewards coming,
- 29:14so it would have the same
- 29:17average reward expectation.
- 29:18And then the animal might receive a reward
- 29:21better than average worse than average,
- 29:24you know,
- 29:24just slightly better than average.
- 29:26So this might also map onto what
- 29:29you might predict you would see
- 29:31with an expectation signal.
- 29:33So we were very interested in trying
- 29:35to figure out how could we tell the
- 29:37difference between a signal that might
- 29:39tell us something about if the animals
- 29:42using it to to read out violations of
- 29:45expectations or alterations of expectation.
- 29:49Or is the animal just using the
- 29:50signal to read up?
- 29:52Yes,
- 29:52this is what I like best.
- 29:53This is what I like worse.
- 29:57And this is basically a repeat
- 29:59of what I have just said.
- 30:02So the the way in which David decided
- 30:04to tackle this was to collaborate
- 30:06with the lab of Jeremiah Cohen,
- 30:09also at Johns Hopkins University,
- 30:11and his then MD PhD student Bill Albari.
- 30:14And so Jeremiah and Bella had been
- 30:16using quantitative models to try
- 30:19to explain the activity of neurons
- 30:22and what what they care about.
- 30:24And so in this way you might use a
- 30:26quantitative model and try to fit
- 30:28the firing rate of given neurons
- 30:30to aspects of your model to try to.
- 30:32Understand what those neurons are encoding,
- 30:35and so this is what Belal and David
- 30:38together in collaboration did to
- 30:39ask if there was any impact of
- 30:42expectations on firing at the time
- 30:44the animals drinking the reward and
- 30:47the way they did this was to look
- 30:49at the to the canonical Rescorla
- 30:52Wagner model that that tells us how
- 30:56predictions are updated by experience.
- 30:59And so this is the reward prediction
- 31:02error framework that many of you
- 31:04are familiar with and in this
- 31:06framework the expected value and
- 31:08animal holds for upcoming reward.
- 31:11Is updated iteratively,
- 31:13so with every experience based on
- 31:16whether the rewarded receives mattress,
- 31:18that or is better, or is worse,
- 31:21and so that's where we have
- 31:22positive prediction error if it's
- 31:24better than expected, no change.
- 31:25If it's the same as expected,
- 31:27negative prediction error is what you
- 31:28get is worse than what you thought,
- 31:30and through the use of this canonical.
- 31:34Way of explaining what the
- 31:37activity of a neuron might be,
- 31:41we can compare that with a much simpler idea,
- 31:44which is that the readout at the time,
- 31:46the animals drinking that reward just
- 31:48reflects a difference in outcome.
- 31:49A binary difference,
- 31:51sucrose versus maltodextrin?
- 31:53Or is this bike activity that we see at the
- 31:56time of reward unrelated to either of these?
- 31:58Of course,
- 31:59we already have an idea that many of
- 32:00the neurons do care about rewards,
- 32:02so we don't expect that that that will be a.
- 32:05Huge contributor so you can take this
- 32:07bike activity of neurons through time.
- 32:09Trial by trial.
- 32:10Look at how the neuron responds to the
- 32:13reward and see if its activity matches
- 32:15just a real time difference in outcome.
- 32:18Or does it actually account for what
- 32:20the animal received a trial before
- 32:22trial before the trial before,
- 32:24as in a Rescorla Wagner model,
- 32:26and I wouldn't be saying this if
- 32:28we hadn't indeed found a group
- 32:31of neurons that does care about
- 32:33what reward the animal received.
- 32:35The trial before the trial that
- 32:37they're experiencing that reward.
- 32:38In other words,
- 32:39there's an impact of experience.
- 32:41So in about 20% of these neurons
- 32:43that fire at the time of reward
- 32:46showed an impact of expectation,
- 32:48another slightly more than 20%,
- 32:51were just encoding the current outcome.
- 32:53This ones better, that was worse,
- 32:55and that was relatively stable.
- 32:57And then there were neurons that didn't
- 32:59care about either of those things,
- 33:01and you can now look at the neural
- 33:03activity of these different.
- 33:05Classes we now divided up our neurons
- 33:07that respond to rewarding to these
- 33:09two classes and get a nice feel for
- 33:12what this actually might look like.
- 33:14So here are the neurons that were the reward
- 33:16prediction error neurons they cared about.
- 33:18What happened trial before
- 33:19divide it up just into.
- 33:21The simplest kind of way of
- 33:24thinking about this trials,
- 33:25in which animals get sucrose.
- 33:28After a trial when they got
- 33:30maltodextrin so better than expected,
- 33:32that's this very tall yellow peak.
- 33:34Trials when animals got
- 33:36sucrose after sucrose.
- 33:38Trials when animals got multi dextrin.
- 33:40After maltodextrin and trials when
- 33:42animals got maltodextrin after sucrose
- 33:44so much worse than they thought and you
- 33:47can see this big modulation of firing.
- 33:49That depends on what just happened.
- 33:51So that matches this.
- 33:53This quantitative assessment
- 33:54and the current outcome.
- 33:56Neurons show much less or no modulation
- 33:59around expectation and is quite flat
- 34:02for the unmodulated neurons obviously,
- 34:04and we can do this same analysis in
- 34:06R3 reward tasks and find the same.
- 34:08Outcome where there's a portion
- 34:10of neurons that encode some kind
- 34:13of expectation signal looks like
- 34:14a reward prediction error,
- 34:16and even more neurons do this
- 34:18when you have this larger dynamic
- 34:20range across the rewards,
- 34:21the animals experiencing,
- 34:22which is kind of interesting,
- 34:24we can again get an intuitive feel
- 34:27for how this maps onto neural activity
- 34:30by looking at subsets of the neurons.
- 34:33In this case,
- 34:34if we just look at the neurons that
- 34:36are seem to encode and expectation.
- 34:38Signal and categorize them based
- 34:41on the calculated error.
- 34:43Was it very positive?
- 34:44Was there no error signal on that trial?
- 34:47Was there a negative error signal on
- 34:49that trial and use eight categories for that?
- 34:52We can see this beautiful
- 34:54distribution of signals along the
- 34:57most positive reward prediction error,
- 34:59little error and negative error.
- 35:01So this gives us a sort of intuitive
- 35:04way to think about how firing
- 35:06happening at the time of reward
- 35:08can actually be telling us.
- 35:10Think about what the animal expects,
- 35:12not just what is this particular
- 35:15reward as far as identity.
- 35:18So this this was really exciting to
- 35:20us to find this kind of signal and BP.
- 35:23We're very used to thinking about
- 35:25these kinds of expectations.
- 35:26Signals in dopamine neurons,
- 35:27and we hadn't been expecting to
- 35:29see this kind of thing in ventral
- 35:31pallidum at all,
- 35:32which we thought would be more just
- 35:34a basic readout. This is good.
- 35:36This is bad in real time.
- 35:38So because we saw these signals that
- 35:41match what are teaching signals,
- 35:43signals that are updated over time
- 35:46and reflect what subjects expect,
- 35:48David wondered if he could find
- 35:50any way to see if the animal's
- 35:53behavior changed based on what the
- 35:55error signal was on a given trial.
- 35:59So we have to say that the procedure
- 36:01that he designed was not designed
- 36:03to see a lot of rich behavior
- 36:05was designed to have the animals
- 36:08behavior very similar.
- 36:09Each trial. Indeed, you saw that when
- 36:11we were looking at the licking rate,
- 36:13but David did still videotape the animal,
- 36:16so he went back and analyzed their behavior.
- 36:19This is what the Chamber looks like.
- 36:21A typical rat chamber,
- 36:22and where the animals can enter reward port.
- 36:25You know when the queues on and drink
- 36:27the reward and there's an interest.
- 36:29Inter trial interval you
- 36:30know they wander around.
- 36:31They might do. A little grooming,
- 36:33might hang out by the port
- 36:34waiting for the next month.
- 36:35Typical behavior that the rat behaviors
- 36:37among us are used to seeing what
- 36:40David found when he looked at the
- 36:42behavior in detail that on trials when
- 36:45animals had just received the reward,
- 36:48they liked better sucrose.
- 36:50Right after that,
- 36:51the animal tended to hang around the port,
- 36:53waiting presumably for more sucrose.
- 36:55If the animal had just received maltodextrin,
- 36:58they tended to wander off more.
- 37:00It's not a huge effect.
- 37:02We can look at this.
- 37:03This is sort of a way to map
- 37:05individual animal behavior.
- 37:06Looking at a cartoon of
- 37:07the square of the chamber,
- 37:08it's not a huge effect,
- 37:10but you see more color and more black.
- 37:12X is away from the port when
- 37:15it's post maltodextrin sucrose,
- 37:17and this is statistically significant.
- 37:20We see the same pattern when we
- 37:21have the three rewards together.
- 37:23After receiving water,
- 37:24they're more likely to be
- 37:26further from the port,
- 37:27so it's very simple measure of how
- 37:31their future behavior is impacted
- 37:33by the the the the validation
- 37:36of their expectation or getting
- 37:38something less than they expected.
- 37:41Simple small effect,
- 37:42but can we manipulate it by changing
- 37:45these neurons and how they fire?
- 37:48So this was the setting for an
- 37:50optogenetic experiment at David,
- 37:52then performed getting together
- 37:53with Tabatha Kim and Kurt Fraser to
- 37:55graduate students in the lab at that
- 37:57time had now moved on to Charles
- 37:59River and to post up with stuff on
- 38:01the mouth and so they simply asked
- 38:03if they express general adoption.
- 38:05Excited Tori option in neurons
- 38:07in the ventral pallidum and then
- 38:09make those neurons fire.
- 38:11By shining light on them at the time,
- 38:13the animals ingesting reward,
- 38:15can they impact the behavior
- 38:17after the animal got the reward?
- 38:19How?
- 38:19How much the animals likely to
- 38:21hang out by the port?
- 38:22That's so if you excite the neurons,
- 38:25you'd expect to see the animals
- 38:26hang close to the port right
- 38:27after you've excited the neurons.
- 38:29Alternatively,
- 38:29if you express an inhibitory option
- 38:32in ventral pallidum so that you
- 38:35can inhibit BP neuron firing at the
- 38:37time they're drinking the reward,
- 38:40you'd expect to make.
- 38:41The animal more likely to be away
- 38:44from the court after that sort
- 38:46of inhibition of BP,
- 38:49and so that's the kind of
- 38:50experiment that they designed.
- 38:51This is just a cartoon by lateral
- 38:54inhibition of BP neurons is going to
- 38:56occur at the time of reward delivery
- 38:59or unilateral excitation of DP.
- 39:01Neurons will occur at the time
- 39:03of reward delivery.
- 39:04So for this experiment the
- 39:06reward is the same in all trials,
- 39:08it's sucrose, but the optogenetic.
- 39:11Activation or inhibition will
- 39:12just occur on half of the trials,
- 39:15so that lets you see if your change
- 39:17in neural activity is affecting the
- 39:19animals behavior independent of the
- 39:21specific taste of the reward, etc.
- 39:23So you hold the those aspects of
- 39:25the reward constant and see if
- 39:27you're turning up or turning down
- 39:29of the neural activity impacts
- 39:30their behavior as you would expect,
- 39:32and that is exactly what they
- 39:34observed in this Chamber.
- 39:36That reward port is here on the right side,
- 39:38on trials in which BP was activated.
- 39:41The subject tends to hang out
- 39:43closer to the port,
- 39:44so a lower value here than on
- 39:47trials where the subject was not
- 39:49did not receive VP activation,
- 39:51so it's a within subject within
- 39:53session comparison and we see
- 39:55the opposite with inhibition.
- 39:57They tend to be further away if you
- 39:59inhibit while they're consuming the reward,
- 40:01so this these are reliable effects but
- 40:05granted relatively small in this procedure,
- 40:07not really used to.
- 40:09Think about how this impacts decisions,
- 40:12but because we saw the signal
- 40:14in this behavior,
- 40:15we wanted to see if we could find
- 40:17any evidence that an expectation
- 40:19reward prediction, like signal,
- 40:21impacted future behavior and this
- 40:23evidence was there and so that
- 40:25was really exciting to us and
- 40:27lead the stage for our continued
- 40:29experiment that I'll tell you about.
- 40:31Right now I'm going to take a quick
- 40:33interim summary and this also be a good time,
- 40:36if anyone.
- 40:38Wants me to clarify something that I've said.
- 40:42So I wanted to just say from these.
- 40:44So far we've learned that the signal
- 40:46in VP that responds to reward is
- 40:48sensitive to pass reward history
- 40:50and can provide a reward prediction
- 40:53error signal to update the animals.
- 40:55Expected value of reward.
- 40:58And So what we would like to know,
- 40:59of course, is, are these signals used?
- 41:02Do they interact with decision processes?
- 41:05Can they impact the actions animals make?
- 41:07'cause that's ultimately what we
- 41:09want to explain how our choices made,
- 41:11what's going on in the brain when
- 41:15the animal evaluates the options?
- 41:18Question yes please.
- 41:20That's really beautifully done.
- 41:22I was wondering if you've
- 41:24looked at what happens with.
- 41:26Obviously, this is a learning signal.
- 41:29What if it's a pharmacologic reward that
- 41:32is sensitive to to tolerance effects?
- 41:35Or you know any sort of reduction?
- 41:38What happens to these neurons and to behavior
- 41:41'cause ya'll animal moved away
- 41:42when they were not getting,
- 41:44you know, beautiful extinction.
- 41:45But that is not obviously what
- 41:47we see when when people are
- 41:49beginning not even addicted.
- 41:50Just beginning to come
- 41:53to start to really like and
- 41:55escalate their their use.
- 41:57Yeah, that's a great question.
- 41:59So what we have not done yet is
- 42:00is used Ivy drug, for example,
- 42:02or even alcohol in this exact model.
- 42:05And we we we need to do that
- 42:07because here we are beginning to
- 42:09define for ourselves what these
- 42:11neurons are doing to natural reward.
- 42:13And when natural reward choices
- 42:15are occurring.
- 42:15But the critical question is how are
- 42:18these processes altered when that reward?
- 42:21Is a drug reward that has pharmacological
- 42:23properties that are quite different
- 42:25and there is important data from,
- 42:27for example,
- 42:27Megan Creed and Christian looser,
- 42:29showing that drugs like cocaine change
- 42:33synaptic efficacy between, for example,
- 42:36the accompagnes and ventral pallidal neurons.
- 42:39So there are chronic effects of drugs
- 42:42on the way that these neurons should
- 42:44be activated and should be firing
- 42:47and and so finding that intersection
- 42:49and studying that was, is it?
- 42:51Ago.
- 42:55OK so I'm gonna go on and tell you about
- 42:59the next reward behavioral procedure
- 43:02where David extended this work to try
- 43:05to understand how these signals are.
- 43:07Even if these signals matter.
- 43:09As far as the choices the animals make,
- 43:11and so he again turns to the notion of.
- 43:14Reward choice, so you need more
- 43:16than one reward at the same time
- 43:18and to provide a setting where he
- 43:19could try to understand choice.
- 43:21He thought it would make the
- 43:23most sense to make the animals
- 43:25choices change through the session
- 43:27by changing their motivation.
- 43:29He was interested in understanding how
- 43:32motivational state impacts choice.
- 43:34And I was very interested in this
- 43:36because motivational state and how
- 43:38it might be relieved by rewards you
- 43:40choose is a nice analogy for eventually
- 43:43thinking about how drug craving may
- 43:45operate within the system and how
- 43:47taking drugs may reduce craving.
- 43:49And then what happens in the brain.
- 43:51So that's the Longview.
- 43:52But in the short view,
- 43:54David wanted to ask specifically
- 43:56about motivational state shifts
- 43:58and how they may impact animals
- 44:01decision making through this system.
- 44:04Where we're recording expected
- 44:06reward and reward preference,
- 44:08and so he's doing this in the
- 44:10face of a shift in thirst.
- 44:12So obviously whether you choose
- 44:14food or water will depend on
- 44:15if you're hungry or thirsty,
- 44:17so this is a a kind of motivational
- 44:19shift that has great relevance to
- 44:20the natural functioning of this
- 44:22circuit and that we thought would
- 44:24help us understand the natural
- 44:26functioning of this circuit.
- 44:27So David developed what he called
- 44:30the dynamic preference task.
- 44:31So this task is very simple.
- 44:34Some animals are choosing between sucrose
- 44:37and water reward by pressing a lever.
- 44:41They begin each day,
- 44:42thirsty and within the session
- 44:45they assuaged their thirst.
- 44:47And within this session,
- 44:49besides the choice trials,
- 44:51it's critical that David also had
- 44:53forced choice trials where throughout
- 44:56time the animals had to experience
- 44:59water and experienced sucrose
- 45:00so that he could monitor the VP
- 45:03signals to that through the session,
- 45:05and so this will become clear when I
- 45:08explain again how this procedure works.
- 45:10So animals rats are in the
- 45:13behavioral chamber, their electrodes,
- 45:15and their ventral pallidum 60.
- 45:17Percent of the trials they receive
- 45:19over an hour and a half are the
- 45:21same as what we talked about before.
- 45:22There's a queue that tells them go
- 45:25to the reward port and 50% of the
- 45:27time they get sucrose. 50% water.
- 45:28It's randomized.
- 45:29They don't know what it will be.
- 45:31These are the forced choice trials
- 45:33they have to complete this to go
- 45:35on to the next trial.
- 45:3740% of the time they hear a cue that
- 45:39tells them it's a choice trial.
- 45:41They get to pick if they get.
- 45:42If they receive sucrose or water
- 45:45by pressing the relevant lover.
- 45:47So we have a mix.
- 45:48Of these outcome choice trials where
- 45:50we can see their behavioral preference,
- 45:52what do they want at that moment in time?
- 45:55And we also have the forest
- 45:57trials where we can't
- 45:58see their preference from their behavior,
- 46:00but instead we can look at how their
- 46:03neurons respond to the two rewards and
- 46:05see if it changes as their choices change.
- 46:07So we use both of these kinds of
- 46:10trials to get important behavioral and
- 46:13neural data that we want to relate.
- 46:15And what you see behaviourally when
- 46:17you look at the responses of a rat
- 46:19in this kind of procedure is that
- 46:22they start out choosing water.
- 46:23That's the long purple lines.
- 46:25This is session time and the
- 46:27number of trials which makes sense.
- 46:29They're thirsty, they're going to
- 46:30press on the water level quite a bit,
- 46:31and as they get less thirsty,
- 46:33they'll press on the water level less.
- 46:35They'll press on the sucrose level
- 46:37lever a few times in the beginning,
- 46:40but that increases overtime
- 46:42as they become less thirsty,
- 46:44so there's a shift in their choices,
- 46:48and you can graph that with this green line,
- 46:50which shows their relative preference.
- 46:52The short black lines tell us
- 46:54when the forced trials occurred,
- 46:56so you see they're forced to sample sucrose
- 46:58and water throughout the whole session,
- 47:01and we see there be choice behavior
- 47:03through these choice trials.
- 47:05So in all of the subjects.
- 47:06Used in this study that I'll talk about.
- 47:08We see a similar shift in preference
- 47:12through the behavioral session,
- 47:14so as they become less thirsty,
- 47:16they tend to just respond for the supers,
- 47:18which makes sense.
- 47:20So we see this behavioral shift.
- 47:23What about the neurons in the
- 47:25VP and so to to address this,
- 47:28David looked again at this response
- 47:31to reward that VP neurons emit,
- 47:34so he's looking at this time
- 47:36period just after reward delivery.
- 47:38When many neurons fire spikes when they
- 47:41get reward and he's using now a general
- 47:45little mini linear excuse me model to
- 47:48try to understand which aspect of of.
- 47:51Uhm,
- 47:52the the design best captures how neurons
- 47:55fire through session time trial by trial.
- 47:59Do they just tend to show a difference?
- 48:01Reflective of the difference in outcome?
- 48:03Sucrose versus water that's
- 48:05relatively stable over time.
- 48:06Do they just show a decrement
- 48:08or increase in activity?
- 48:10They start satiety as you move through time.
- 48:12Or is there an interaction
- 48:14between these two processes?
- 48:16And by looking at this statistically,
- 48:18David founded sizeable proportion
- 48:19of neurons that care about.
- 48:22Both of these at the same time,
- 48:23so their activity fits best.
- 48:25Changing preference through time,
- 48:27so some something to do with satiety.
- 48:31Presumably something to do with preference.
- 48:34And that makes sense.
- 48:36'cause that's what happens to
- 48:37the behavior with the behavior
- 48:38switches as you move through time.
- 48:40The animals preference for
- 48:42water versus sucrose switches as
- 48:44they become less thirsty,
- 48:46and so here on the left,
- 48:47if you can see this might be hard,
- 48:50but I'll describe it for you is just
- 48:53an example to show 1 neuron firing
- 48:56in a very typical way for the whole
- 48:59population through the session.
- 49:01So at the beginning we have
- 49:03neuron spiking at session.
- 49:05The first trials and at the end
- 49:07the last trials and these shaded
- 49:09areas are the times when the animals
- 49:11drinking sucrose or drinking water.
- 49:13These are the times analyzed and you can
- 49:16see that as the animal first gets sucrose,
- 49:19you see moderate spiking.
- 49:21That increases overtime when
- 49:22the animal first gets water,
- 49:25you see a lot of spiking.
- 49:26That really decreases overtime.
- 49:28If you look at this same kind of
- 49:32feature overtime for all of the neurons.
- 49:35Plotted here in these two figures,
- 49:37with the sessions divided into quarters,
- 49:39quarter 1234,
- 49:40you see that the mean reward response to
- 49:44sucrose is moderate and then gets bigger.
- 49:48You see that the mean response to
- 49:50water starts big and positive and
- 49:52gets smaller and more negative
- 49:54as the session goes on.
- 49:56So you can see this much more easily
- 49:58if we think about the mean here
- 50:01over quarters for sucrose versus
- 50:03water in this final graph down here,
- 50:05the bend firing rate,
- 50:07we can see the increase in activity for
- 50:10sucrose overtime in one session and
- 50:13the decrease in mean activity for water.
- 50:17So this is interesting because we
- 50:19see that the neural activity sort
- 50:20of shifts more excited for water
- 50:22in the beginning,
- 50:23more excited for sucrose at the end.
- 50:26And so does the animals preference, right?
- 50:28The preference of the animus shift similarly.
- 50:31But another thing to note is
- 50:33this isn't like a mirror image.
- 50:35These two curves are exactly symmetrical.
- 50:38This water line really decreases,
- 50:40and the sucrose mine is kind of flat,
- 50:42so this was pretty interesting
- 50:44and I just thought, well,
- 50:46that's the way the data are,
- 50:47but given David's beautiful more sort
- 50:50of quantitative mind, he thought,
- 50:52well, what?
- 50:53How can I explain that particular shape?
- 50:55Is there a way I can characterize?
- 50:57That quantitatively and he started
- 50:59thinking about whether this reward signal
- 51:02that signaling a reward prediction error.
- 51:05Could it contain more information
- 51:07than just something related to what
- 51:09reward did I get on the last trial?
- 51:11Could it also reflect the value of the
- 51:14whole task as the animals becoming sated?
- 51:18So,
- 51:18so every every both rewards will
- 51:20become less valuable in some sense,
- 51:22as the animals becoming less thirsty.
- 51:26So I I really thought this was a
- 51:28beautiful insight that he had and he
- 51:31developed based on his prior work with Bill.
- 51:33All models again to fit to the
- 51:36activity of each neuron to see which
- 51:39kind of quantitative model best
- 51:41explained the way the neurons fired
- 51:43and on the left you see the firing
- 51:46rate in a session of an example
- 51:48neuron just to remind us it's the
- 51:50increase in response to sucrose at
- 51:52at the top that orangey red line
- 51:54is moderate and there's a sharp.
- 51:57More dramatic decrease in responding
- 51:59to water in this blueish purple
- 52:01line and so you can ask if just
- 52:04a simple straight line satiety.
- 52:05Does that explain best the
- 52:08way the firing changes?
- 52:09Is the firing explained best
- 52:11by a preference switch that
- 52:13it would be perfectly symmetrical
- 52:15preferences just from you know zero to 1?
- 52:18Or what about both of these together?
- 52:21And so for David,
- 52:23that's just a linear combination.
- 52:25Of models describing both of these,
- 52:27and then when you combine these literally,
- 52:30it looks like this and you know you can
- 52:32see where I'm going because already the
- 52:34model shape looks similar to the neural
- 52:36shapes that we've been looking at.
- 52:38And so here again, is the model.
- 52:39When he looked at each neuron and fit its
- 52:42activity to these three different models,
- 52:44he finds that the best fit
- 52:47model is this mixed model.
- 52:49What this means is most neurons
- 52:51seem to care about both satiety,
- 52:53so movement through the session in time
- 52:56and their current preference for reward,
- 52:59which one they're liking better.
- 53:02So that's pretty cool.
- 53:04So, So what David showed us is
- 53:06that it's not just the immediate
- 53:08difference in reward value that is
- 53:10being reflected in this activity,
- 53:12but there's also an impact of satiety.
- 53:16So this was all analyzed based
- 53:19on forced trial data, right?
- 53:21'cause we're looking at how the animal
- 53:24responds to the reward through session time.
- 53:26But we have all of these choice
- 53:28trials for the animals making its
- 53:30own decision about which reward
- 53:32it wants at that given time,
- 53:35and what David wondered is,
- 53:37do these responses of the neurons
- 53:39that tell us how much the animal,
- 53:42what the animal thinks about the
- 53:45reward relative to its expectation,
- 53:47does that have anything to do
- 53:49with their behavior?
- 53:50Because in the end,
- 53:51we'd like to try to get an understanding
- 53:53of how these systems impact choice.
- 53:56That's our eventual goal.
- 53:57So the way that David decided
- 54:00to think about that.
- 54:02Was to look at the animals behavior.
- 54:05Good idea to do first.
- 54:06Here are three rat examples on the left.
- 54:09You're looking at the animals choice
- 54:11behavior through the session.
- 54:13The purple bars on the bottom are
- 54:15when the animal chooses water
- 54:16they do a lot at the beginning,
- 54:18last moving forward,
- 54:19and then they shift and tend to choose
- 54:21sucrose more as session time goes on.
- 54:23So you can plot the preference curve.
- 54:25For sucrose,
- 54:26the preference curve for water
- 54:28and you see this.
- 54:29This kind of function and you can see
- 54:32that example for three different rats.
- 54:34When David looked at the neural
- 54:37estimates for this mixed model that
- 54:41came from what he calculated here,
- 54:44it came from these forced trials
- 54:45and tried on a trial by trial
- 54:48to estimate what the animal's
- 54:50preference was just based on the
- 54:52neural activity for a given neuron.
- 54:54Then averaging that across
- 54:55all neurons from a given rat,
- 54:58you get these preference curves,
- 54:59and they're just remarkably similar.
- 55:02You don't even need all the beautiful
- 55:04statistics to tell you the neurons
- 55:06are giving the same readout of
- 55:08what the animals preferences,
- 55:10moment by moment as the decision
- 55:12the animal makes.
- 55:13So this is a really nice correlation
- 55:16that helps us build on the idea
- 55:19that this signal is important for
- 55:21the animals future decision, and.
- 55:23That's what these graphs
- 55:25here on the right support.
- 55:27If you look at the correlation for each
- 55:29neuron of its activity with the animals
- 55:32preference for neurons like that.
- 55:34Were weighted within this mixed model
- 55:36that care about outcome in time.
- 55:38We see that correlation is very
- 55:40close to one for very many of them.
- 55:42If you ask from the neural
- 55:45activity when in time this switch
- 55:47point might be for each neuron.
- 55:50Many neurons that care about outcome
- 55:53by time give you a very close
- 55:56estimation of the actual trial.
- 55:58So the switch point is zero.
- 55:59You can see many or within 20 trials.
- 56:03So, quantitatively,
- 56:03we've got a really nice agreement
- 56:06between neural activity with the
- 56:09animal actually decides to do.
- 56:11So that led save it again to turn
- 56:13to optogenetics to see if he
- 56:16could manipulate the system and
- 56:18manipulate the subjects choice.
- 56:19And this is the final little bit of data,
- 56:22bit of data that I'll be showing you and
- 56:24then we can discuss this as you wish.
- 56:27So now David wants to see if by
- 56:29controlling ventral pallidal neuron
- 56:31activity he can impact their choice,
- 56:34so so he has optimal control.
- 56:37He's now not going to make the
- 56:40animals thirsty, he's just going to.
- 56:41Go back to the situation where they're
- 56:43choosing between sucrose and maltodextrin.
- 56:46This is a situation when they're not
- 56:48thirsty and their behavior through
- 56:49the session tends to be relatively
- 56:51stable and he wants the behavior
- 56:54to be relatively stable because now
- 56:56he must to go in and try to change
- 56:58it by messing with the BP reward
- 57:00prediction error signal.
- 57:01So what he's going to do is express
- 57:04channelrhodopsin in ventral palatal
- 57:06neurons and shine light on them
- 57:08to force them to fire every time
- 57:11the animal drinks maltodextrin.
- 57:13And in the procedure that he uses,
- 57:15there's going to be forced choice
- 57:17trials so he can continue to make
- 57:20them drink maltodextrin paired with
- 57:22stimulation and choice trials so we
- 57:25know what the animal actually would
- 57:27prefer to drink at any given time.
- 57:30First. Animals are well trained.
- 57:32Then there is a session of
- 57:35optogenetic manipulation,
- 57:36so well trained animals.
- 57:37And this is just a diagram of when
- 57:40the stimulation occurs.
- 57:41So at the time that the animals actually
- 57:43drinking maltodextrin and we can talk
- 57:45about that more if you want to later.
- 57:47So if you look at baseline here on the right,
- 57:50a well trained animals prefer sucrose
- 57:52based on their choice trials.
- 57:55The press the lever to get sucrose
- 57:56most of the time and the blue dots are
- 57:58the subjects in which were expressing
- 58:00channelrhodopsin the Gray dots or
- 58:02subjects expressing the empty vector GFP.
- 58:04Both animals get laser shining in
- 58:07their brain, but the great author.
- 58:09Our control.
- 58:10In the test session,
- 58:11the next day after baseline were
- 58:13stimulating every time the subject gets
- 58:16less preferred reward maltodextrin and
- 58:18what you see is it shifts preference
- 58:21towards maltodextrin on the choice trials.
- 58:23So what does that actually look
- 58:26like through time?
- 58:27Here is one session, the test session.
- 58:30And here's the smoothed preference
- 58:32based on liver choice for each rat
- 58:35Gray or the controls relatively stable.
- 58:37They mostly want sucrose and blue.
- 58:40Are these experimental animals where we've
- 58:42shifted the preference to maltodextrin,
- 58:44and you can see it's a gradual effect
- 58:47that accrues through experience,
- 58:49so it's not that the first
- 58:50time you stimulate it,
- 58:51they immediately shift.
- 58:52This is congruent with the idea.
- 58:54But it's a learning response.
- 58:56You're sending them a signal that
- 58:57that reward is better than expected.
- 58:59So maybe you should change what
- 59:01you do on upcoming trials.
- 59:03And also congruent with the idea
- 59:05that this is a learning signal.
- 59:07This behavior change does last
- 59:10until the next day.
- 59:12So the test day is the day of optogenetic
- 59:16manipulation Recovery day one.
- 59:18We just see what their choices are
- 59:19and you can see that their choices
- 59:21still tend to be more towards
- 59:23maltodextrins then they weren't
- 59:25baseline and this changes over time
- 59:27as we no longer ascending that
- 59:29fake signal that we could send
- 59:31with the optogenetic manipulation.
- 59:33So this is a first step to providing
- 59:36some evidence that this signal can
- 59:39impact the animals future choice.
- 59:41So if you look at their.
- 59:43Latency's to choose levers in the
- 59:46optogenetic stimulation experiment,
- 59:48you find that overtime they tend
- 59:51to choose maltodextrin more,
- 59:53and on trials choice trials when
- 59:55they're going to choose maltodextrin,
- 59:56their latency to go to the lever
- 59:58to make that choice is faster,
- 01:00:00so we see this change in behavior
- 01:00:03that matches what you would expect.
- 01:00:05For this. This kind of signal.
- 01:00:08So what I told you is that initially
- 01:00:10we see a signal in the ventral
- 01:00:12pallidum when animals are actually
- 01:00:14experiencing reward ingesting it.
- 01:00:16That seems to match their relative
- 01:00:19preference at that current time,
- 01:00:21and if you analyze that spike activity
- 01:00:23relative to the current time and
- 01:00:25the reward period just before that,
- 01:00:28and just before that,
- 01:00:29and just before that,
- 01:00:30IE reward history,
- 01:00:31you see that at least a subset of these
- 01:00:34care about reward history and what
- 01:00:36they instead are signaling is a reward.
- 01:00:38Prediction error is what I just
- 01:00:40got better than I expected.
- 01:00:42The same or worse.
- 01:00:44And so these same signals,
- 01:00:47these moment by moment reward
- 01:00:50prediction error signals also care
- 01:00:52about the current motivational state.
- 01:00:55They're also able to integrate the
- 01:00:57larger change in value that might
- 01:01:00happen as your motivational state is
- 01:01:02changing as you get less thirsty.
- 01:01:04And hypothetically,
- 01:01:05in other situations we haven't tried yet,
- 01:01:07right?
- 01:01:08As your craving might be reduced
- 01:01:10as you ingest drugs.
- 01:01:12As hunger changes as you eat etc and
- 01:01:15so these signals that occur at the
- 01:01:18time the animals ingesting reward,
- 01:01:20they affect their future behavior.
- 01:01:22As we saw in that very simple.
- 01:01:25A measure of how close you are to the port,
- 01:01:28and as we saw in this,
- 01:01:29perhaps more informative choice
- 01:01:31procedure where animals are choosing
- 01:01:33which lever to push in order
- 01:01:36to get the reward that
- 01:01:37they want at that time.
- 01:01:39And so the big question,
- 01:01:40of course, is what?
- 01:01:42What do these signals mean
- 01:01:44for the circuit as a whole?
- 01:01:46So if I go back to the statement that
- 01:01:48I made at the beginning that usually
- 01:01:50the ventral pallidum was the more
- 01:01:52boring area that was just the output.
- 01:01:54For the fantastically interesting
- 01:01:56nucleus incumbents, of course,
- 01:01:58the nucleus of Cummins is
- 01:01:59fantastically interesting.
- 01:02:00But these are big excitatory signals
- 01:02:02in the ventral pallidum unlikely to
- 01:02:04be driven by the Gabaergic medium.
- 01:02:06Spiny neurons of the nucleus accumbens,
- 01:02:08and when we look.
- 01:02:09And David didn't record in
- 01:02:10the comments as well.
- 01:02:12When we look there,
- 01:02:13we don't see the large numbers of
- 01:02:15neurons representing this reward
- 01:02:16prediction error in the same way,
- 01:02:18so it's a it's a signal can built
- 01:02:21here in the VP most likely by
- 01:02:24integrating various inputs important
- 01:02:26new work from Megan Creed's lab at
- 01:02:28Saint Louis in Saint Louis showed
- 01:02:31that projections from the ventral
- 01:02:33pallidum back to the nucleus.
- 01:02:35Incumbents in fact,
- 01:02:36might be really important when animals
- 01:02:38are making decisions to consume.
- 01:02:41Rewards so,
- 01:02:42so the VP has a really interesting
- 01:02:44relationship with the rest
- 01:02:46of the reward circuitry,
- 01:02:47their inputs to to BTI and VTA projects,
- 01:02:50to ventral pallidum,
- 01:02:51and so,
- 01:02:52so how these signals that we
- 01:02:54identified fit in with the rest
- 01:02:56of the activity of the reward
- 01:02:58circuit is a really important.
- 01:03:00Future direction as well as trying
- 01:03:02to map how the circuit response
- 01:03:05to natural reward with how it
- 01:03:07might respond to drug reward.
- 01:03:09Because, as mentioned,
- 01:03:10your eventual goal is to try to
- 01:03:13understand these interactive
- 01:03:14processes and how they modulate
- 01:03:17in humans are seeking of things.
- 01:03:19We should seek our food rewards and
- 01:03:22think and seeking of rewards that in
- 01:03:25some individuals can become unhealthy.
- 01:03:28So I think I'll I'll stop there
- 01:03:31with just thinking again.
- 01:03:32The lab members that participated
- 01:03:35in this work,
- 01:03:37and I identified David and Jocelyn early.
- 01:03:40They're really the main drivers of this.
- 01:03:42I also showed Jude members of
- 01:03:44Jeremiah coincide that were important,
- 01:03:46but I want to thank the lab in
- 01:03:48general for all of their input for
- 01:03:50this work and lab meetings and
- 01:03:52and helping one another conduct
- 01:03:54all of the experiments that I
- 01:03:56want to thank funding from NIH,
- 01:03:57of course,
- 01:03:58and I want to.
- 01:03:59Thank you all very much for giving
- 01:04:01me the opportunity to talk about
- 01:04:03this basic neuroscience research
- 01:04:05and I hope it gives us all some
- 01:04:07ideas about how we can think about
- 01:04:09basic nice neuroscience work and
- 01:04:11how it can tell us about the human
- 01:04:14condition and how we doing basic
- 01:04:16neuroscience work can learn and shape
- 01:04:18what we do based on the human condition.
- 01:04:21So thanks very much.
- 01:04:24Thank you so much, I'm really
- 01:04:27enjoyed that and I have encouraged
- 01:04:29the trainees to ask questions first
- 01:04:32if there's any trainees out there.
- 01:04:35Doctor Taylor is a trainee of
- 01:04:37course lifelong but may not qualify,
- 01:04:40so I would like to start with with a trainee,
- 01:04:43but if not we can we can get
- 01:04:46to questions from UN trainees.
- 01:04:51Doctor Taylor's training
- 01:04:55all right? Well then that seems appropriate.
- 01:04:57Doctor Taylor.
- 01:04:57Why don't you kick us off?
- 01:04:59I have questions too. Sorry, I come.
- 01:05:03If a trainee wants to interrupt me,
- 01:05:06please go ahead.
- 01:05:08That was a beautiful talk as always Patricia.
- 01:05:12So I have a question which is
- 01:05:16sort of how dynamic do you think
- 01:05:20these VP responses are in that.
- 01:05:24I wonder whether you would see
- 01:05:28similar VP signals related to
- 01:05:31expectation and prediction error.
- 01:05:33If you in your experiment initially
- 01:05:36looked at sucrose preference
- 01:05:39compared to something that was
- 01:05:41actually slightly aversive,
- 01:05:43like a salt solution,
- 01:05:46but then made the animals
- 01:05:49physiologically salt induced the salt,
- 01:05:53a salt state for that reward.
- 01:05:56Would you see the VP neurons suddenly
- 01:06:00switch over to tracking the?
- 01:06:03The salt, uhm?
- 01:06:06Yeah, yeah, I I think so and so.
- 01:06:09So although this is a history
- 01:06:12dependent signal that follows this
- 01:06:14reward prediction error kind of model,
- 01:06:16we think it's not model free but it's
- 01:06:19more model based in that the subject
- 01:06:21can update it on the fly and so so you
- 01:06:26know in in this procedure animals start
- 01:06:29each day thirsty and then become sated.
- 01:06:32Their response to in a different
- 01:06:34procedure where there's a water
- 01:06:36predictive cue their response to the
- 01:06:38water predictive cue is very high
- 01:06:40at the beginning of each session,
- 01:06:42even though it's very low by the
- 01:06:44end of the session when thirst is
- 01:06:47is no longer a drive.
- 01:06:48So so there.
- 01:06:49I'm not sure if I'm getting to exactly
- 01:06:51what what your question was, but it's a.
- 01:06:53It's a super dynamic system and
- 01:06:55I think immediately impacted
- 01:06:57by the animals expectations,
- 01:06:59and it doesn't necessarily have to accrue.
- 01:07:03Overtime it's impacted by
- 01:07:04what happens over time,
- 01:07:06but also can be directed by a more
- 01:07:08sort of cognitive, a goal directed.
- 01:07:12Uh, evaluation.
- 01:07:16List Europe.
- 01:07:19Thank you for that amazing talk
- 01:07:20and I'm curious 'cause we've
- 01:07:22been talking about expectation,
- 01:07:23but most of the signal that
- 01:07:25you've been presenting is
- 01:07:26during the reward consumption.
- 01:07:28So do you see anything when the
- 01:07:30queue is present that indicates the
- 01:07:31outcome that they're expecting before
- 01:07:33they can compute the prediction
- 01:07:34error? Yeah, that's a great question.
- 01:07:37So in this particular set of
- 01:07:39studies that I told you about
- 01:07:41the queues were not informative.
- 01:07:42As far as the identity of the reward,
- 01:07:45so we didn't find very
- 01:07:47much expectations signal.
- 01:07:48In those cues,
- 01:07:50but David ran a variation in this
- 01:07:52dynamic thirst task where there
- 01:07:55were accused that came before
- 01:07:57each of the rewards in the forest
- 01:07:59trials of a water Q&A sucrose Q,
- 01:08:02and in that case the reward prediction
- 01:08:04error like signaling transferred
- 01:08:06to the queue as you would predict,
- 01:08:09and so that expectation related
- 01:08:11response was mostly there in
- 01:08:13the in the way that the animal
- 01:08:16responded to the queue.
- 01:08:17So that's that's a great question.
- 01:08:20Al
- 01:08:22hi, thanks so much for the talk.
- 01:08:24I was really struck by your result
- 01:08:26that about heterogeneity in terms
- 01:08:28of value versus RPE signals in
- 01:08:31the ventral pallidum, especially
- 01:08:33given this kind of ongoing
- 01:08:36discussion about whether a Cummins
- 01:08:38concentration of dopamine represents
- 01:08:39a value or RPE signal,
- 01:08:41and in particular. Some
- 01:08:45recent work of Sam Gershman,
- 01:08:46and now, which is showing that
- 01:08:47you can maybe reconcile these
- 01:08:49approaches by having a kind of RP
- 01:08:52signal with sensory feedback that
- 01:08:54looks like a value signal.
- 01:08:56And so I guess I was just served as an
- 01:08:58open ended question.
- 01:08:59Wondering whether you think that this
- 01:09:01heterogeneity in the ventral pallidum
- 01:09:03might somehow either resolve this
- 01:09:05discrepancy or correspond to as well
- 01:09:07the concentration of dopamine dynamics.
- 01:09:10Yeah, that's a great question and
- 01:09:12my short answer is is I I don't
- 01:09:15know so it is true that this system
- 01:09:17is highly interconnected with the
- 01:09:20canonical dopamine neurons in the VTA.
- 01:09:23So VP neurons project back to the VTA,
- 01:09:25both to dopamine neurons but also to
- 01:09:28the GABA interneurons and the VTA.
- 01:09:30Dopamine neurons do send a projection to VP,
- 01:09:33so there's some interaction in the
- 01:09:35creation of these kinds of dopamine
- 01:09:37signals that that we think about as far
- 01:09:39as our PE and. What exactly are they?
- 01:09:42Are they communicating to the incumbents?
- 01:09:44Uh, so I.
- 01:09:45I don't know how that's all gonna
- 01:09:47workout as far as trying to see if
- 01:09:49there's a separable value signal or
- 01:09:50whether it's really going to be able
- 01:09:53to be understood all as a readout of
- 01:09:56online changes in what the animal
- 01:09:59is actually actually receiving.
- 01:10:01So I think that's something that's
- 01:10:03still left to work out.
- 01:10:04So really interesting sort of not a problem,
- 01:10:07and the communication between the
- 01:10:09VP and the incumbents.
- 01:10:11Is also going to be a factor.
- 01:10:12Presumably so, uhm.
- 01:10:14I like the direction of your question
- 01:10:18because it forces these results have
- 01:10:22forced me and all this forces us to
- 01:10:25not think of reward prediction error
- 01:10:26is just here in this group of dopamine
- 01:10:29neurons and then medium spiny neurons.
- 01:10:31Maybe all are transmitting expected value
- 01:10:33and this area does this in this area.
- 01:10:35Does that and instead you know
- 01:10:37these systems are all interconnected
- 01:10:39and these variables seem to be
- 01:10:42represented to greater or lesser
- 01:10:43degrees throughout the circuit.
- 01:10:45So beautiful work from the U2 lab,
- 01:10:48in fact,
- 01:10:49has shown has shown that really
- 01:10:51painstaking recording studies,
- 01:10:53so it's it's going to be harder
- 01:10:56to figure out than then.
- 01:10:58I would dream when we want to make a
- 01:11:01nice model where everything is only
- 01:11:03just very very separable and in a
- 01:11:05separate kind of neuron and separate place.
- 01:11:08But that's a nice challenge.
- 01:11:09There's work to do it for the future.
- 01:11:13Ralph, I'm going to ask a quick question
- 01:11:15from the chat and then over to you.
- 01:11:17Media Naseer says rats prefer water
- 01:11:20over sucrose when dehydrated.
- 01:11:22However, some people prefer sugary
- 01:11:24soft drinks over water when thirsty.
- 01:11:26Is there a mechanistic
- 01:11:28difference in this situation?
- 01:11:30That's a great question, and one I I
- 01:11:32don't know the answer to and I would.
- 01:11:34I guess I would immediately wonder
- 01:11:37about the role of long term experience
- 01:11:40in humans for going for the sugary
- 01:11:43soft drink to relieve thirst and
- 01:11:46and whether we could model that.
- 01:11:49By the way we expose our rats
- 01:11:51to these different rewards.
- 01:11:53Overtime probably someone working
- 01:11:55more in the nutrition field
- 01:11:57maybe has already done that,
- 01:11:59so that answer might be known
- 01:12:00and I just don't know it.
- 01:12:01I think that's a great question.
- 01:12:04Ralph, go ahead.
- 01:12:06Trisha really like these
- 01:12:08experiments and great data.
- 01:12:09I have a question
- 01:12:11I guess about the maltodextrin and and what.
- 01:12:15Is this is this purely sensory?
- 01:12:17Is it post ingestive?
- 01:12:18I know the answer is probably both,
- 01:12:19but I guess I'm interested
- 01:12:21because maltodextrins are
- 01:12:22sort of a tricky thing.
- 01:12:23It's not very sweet,
- 01:12:24but of course you can't detect
- 01:12:25it at high concentrations.
- 01:12:27I can't remember
- 01:12:28from from David early paper how
- 01:12:30high it was, and if you know
- 01:12:32that Jamaican detector at least
- 01:12:34taste it. And of course some
- 01:12:35of the signals you're seeing
- 01:12:37or so fast is clearly something
- 01:12:38century. But I wonder if over the session
- 01:12:40they're learning. It has a really
- 01:12:42it gets broken down so quickly into glucose.
- 01:12:44It's guys seeing indexes.
- 01:12:45Is higher than sucrose, right?
- 01:12:47So it should be really fast post
- 01:12:48ingestive signals and so I wonder
- 01:12:50how you how you think about that.
- 01:12:53I think, uh, so David more than
- 01:12:56me really did the important work.
- 01:12:59All you train is out there of
- 01:13:00looking at all the old literature,
- 01:13:02including all the old animal
- 01:13:04behavior literature where people
- 01:13:06have done a lot of work comparing
- 01:13:08tastants and looking at preference.
- 01:13:10Ways to measure preference in assessed
- 01:13:12preference in rodents and so.
- 01:13:13So he he looked carefully at that when
- 01:13:16he chose maltodextrin as a comparison.
- 01:13:18So I think it's a good comparison,
- 01:13:20but we can't.
- 01:13:21It's very difficult to get two
- 01:13:22things that are exactly the same,
- 01:13:24but different, and so in that you know
- 01:13:26in fact that's impossible, right?
- 01:13:29So one thing I can say is that,
- 01:13:32at least for the signal in the
- 01:13:33way that David's looking at it,
- 01:13:35there isn't a change through the session
- 01:13:38in the circumstance when animals.
- 01:13:41Thirsty so there.
- 01:13:42Waters stated,
- 01:13:43and they're just choosing between
- 01:13:45the two rewards that might show that
- 01:13:48there's a big impact of the way that
- 01:13:50that these are is post ingestive
- 01:13:52effects of sucrose versus maltodextrin.
- 01:13:54So so at least on the face of it,
- 01:13:57based on the analysis of this signal
- 01:13:59there during the reward period,
- 01:14:01there's nothing obvious.
- 01:14:02Maybe there are other ways that
- 01:14:04he could look at the signals more
- 01:14:06carefully to see if there is feedback,
- 01:14:09because.
- 01:14:09And as you know,
- 01:14:11you know there's a big literature
- 01:14:13on how post ingestive impacts of
- 01:14:17food impact functioning within
- 01:14:19the striedl circuits that that
- 01:14:20we all know and love so much.
- 01:14:22So I I would,
- 01:14:23I wouldn't say there's not an interaction.
- 01:14:26I.
- 01:14:26I think there probably is,
- 01:14:27but at least for the signal
- 01:14:29that he's looking at,
- 01:14:29he didn't note anything obvious.
- 01:14:32Text.
- 01:14:35I have a quick question
- 01:14:36or maybe not so quick.
- 01:14:38The VP is a surprisingly large
- 01:14:42and heterogeneous structure and
- 01:14:44so I have a couple of questions.
- 01:14:47One is from your recordings.
- 01:14:49Can you determine the cell types
- 01:14:53that are responding based on their
- 01:14:56firing patterns or some kind of
- 01:14:58algorithms that let you know about,
- 01:15:00let's say cholinergic neurons
- 01:15:03versus other types of neurons?
- 01:15:06And then here on top and then
- 01:15:09the other question is around
- 01:15:11a sub sections of the VP.
- 01:15:13Whether you see these kinds of responses
- 01:15:15uniformly throughout the structure,
- 01:15:16or whether the anterior versus
- 01:15:18the posterior is more responsive
- 01:15:20to these value measurements.
- 01:15:24Those are great questions because it's
- 01:15:26known that as as many of you may know,
- 01:15:28the VP contains a lot of
- 01:15:30kinds of different neurons.
- 01:15:31It's it's mostly Gabaergic neurons,
- 01:15:34but there are neurons that release
- 01:15:36glutamate cholinergic neurons.
- 01:15:37It's a mix, and it's an area that
- 01:15:40doesn't have easily discernible
- 01:15:42boundaries necessarily.
- 01:15:43So when people work with it
- 01:15:45in in the rat and the road,
- 01:15:47it's a little bit difficult.
- 01:15:48So one way that David went about that that
- 01:15:51gets to your second point is he tried to.
- 01:15:54Positional has electrodes
- 01:15:55in sort of a central,
- 01:15:57not very anterior,
- 01:15:58not very posterior and operating medial,
- 01:16:00not very lateral area.
- 01:16:01So he could just be in the
- 01:16:03middle of the canonical BP,
- 01:16:04at least as described on Atlas is.
- 01:16:06So that's not necessarily
- 01:16:07what you might want,
- 01:16:09but he did not see variation based on
- 01:16:12electrode placement with his signals.
- 01:16:14Had he gone very far anterior
- 01:16:17posterior to search for that,
- 01:16:19he might have seen that because there
- 01:16:21is work emerging from other labs,
- 01:16:23including the Berridge lab.
- 01:16:25Showing that there the VP plays
- 01:16:27different roles in behavior when you
- 01:16:30are more anterior versus more posterior.
- 01:16:32So that's an important issue
- 01:16:34that that we've not looked at as
- 01:16:36far as cell types in the rat.
- 01:16:38So not having the the beauty of
- 01:16:40the transgenic mouse to let let us
- 01:16:43access different cell types easily,
- 01:16:45like Gabaergic cells,
- 01:16:46glutamatergic cells, etc.
- 01:16:47We can't say for sure.
- 01:16:50The area that he implanted his
- 01:16:52electrodes and his mostly Gabaergic.
- 01:16:55When we you there is a small
- 01:16:58a glutamatergic population,
- 01:16:59though,
- 01:16:59for example that people studied
- 01:17:01that has a critical behavioral role.
- 01:17:03When we use waveform shape and other
- 01:17:06neuro physiological characteristics to
- 01:17:07try to cluster neurons into different types,
- 01:17:10we get mostly,
- 01:17:11you know,
- 01:17:11big amorphous cluster that we presume
- 01:17:14is largely these canonical Gabaergic
- 01:17:16neurons and a smaller cluster that we
- 01:17:19guess could be the glutamatergic neurons,
- 01:17:21because it tends to signal very
- 01:17:24differently from the Gabaergic.
- 01:17:26Iran,
- 01:17:26so it doesn't show any of the same
- 01:17:29kinds of signals that I've just
- 01:17:30talked to you about, but we can't.
- 01:17:32And unless we do something
- 01:17:33you know more rigorous,
- 01:17:35something genetic like using
- 01:17:36viruses somehow in the rat to
- 01:17:38access the different populations
- 01:17:39or redo the work in the house.
- 01:17:41We really can't say for sure,
- 01:17:42and we've not even begun to think
- 01:17:45about or touch on the cholinergic.
- 01:17:48Aspects of this?
- 01:17:48I'm ashamed to admit in front of Marina,
- 01:17:52oh, that's OK. Are there any other questions?
- 01:17:56I don't see anything else in the track chat.
- 01:18:01If not one more please or I'm Joe.
- 01:18:07So I actually have to.
- 01:18:10I guess it's more related
- 01:18:12to technical questions.
- 01:18:13One was that when you used
- 01:18:17optogenetics that you injected.
- 01:18:19Inhibitory virus bilaterally.
- 01:18:22But you injected unilateral for the stimulus.
- 01:18:26So I was wondering why there is
- 01:18:28difference used by letter for
- 01:18:30inhibitory and Union letter for
- 01:18:33stimulus and the second one was.
- 01:18:35So all the dopamine signals were measured
- 01:18:38during the first trial for the
- 01:18:41second half of the experiments.
- 01:18:43And do you think there will be
- 01:18:45difference if you measure the
- 01:18:47signals during the free trials?
- 01:18:49Because Brett doesn't.
- 01:18:50No, what they're going to
- 01:18:52get when they press the.
- 01:18:54Or ignore spoke or pistol ever?
- 01:18:57Yes, those are both good questions
- 01:18:59I I think I would do that.
- 01:19:00The second one first and that is
- 01:19:02so in the in the forced trials.
- 01:19:04The rat doesn't know what reward it will get,
- 01:19:07so that's when we see these big signals.
- 01:19:10That are very different,
- 01:19:11but in the free trials,
- 01:19:12when the animal presses
- 01:19:14the lever for sucrose.
- 01:19:15He or she knows that that that it's
- 01:19:17about to drink sucrose in the in the port.
- 01:19:20So expectation already reduces
- 01:19:22that signal because he knows
- 01:19:24already what's going to happen,
- 01:19:26so that that's a great question,
- 01:19:28and one reason why we didn't look
- 01:19:30at those signals in great detail
- 01:19:32through the session is because
- 01:19:33by the time you get to the end of
- 01:19:35the session is really only enough
- 01:19:37data from the sucrose trials.
- 01:19:39The beginning of the session.
- 01:19:40There's really only enough
- 01:19:41data for the water trials,
- 01:19:43but it would be interesting
- 01:19:44still to show those.
- 01:19:45Think about those more and I
- 01:19:47think that's a nice question,
- 01:19:49and it might be interesting to look
- 01:19:50at the neural activity around the
- 01:19:52lever press for example so that there
- 01:19:54are other other time periods that
- 01:19:56I think I would be interested in.
- 01:19:58Knowing what are those neurons doing
- 01:20:00during those other time periods?
- 01:20:02The first question you asked is
- 01:20:04about the optogenetics procedure
- 01:20:05and so this is just something
- 01:20:07away that we've typically done it,
- 01:20:08and it's probably mostly out of convenience.
- 01:20:10So when you're activating neurons
- 01:20:12you usually can change the animal's
- 01:20:15behavior with.
- 01:20:15Optogenetics because it's such
- 01:20:17a strong and artificial approach
- 01:20:19usually can change the behavior by
- 01:20:22impacting the brain just on one side,
- 01:20:24and it's easier for the experimenter
- 01:20:26because then there's only one connection
- 01:20:28that they need to make with the.
- 01:20:29With the Rotary joint and all of this,
- 01:20:31but when you're inhibiting in a
- 01:20:33particular brain region a lot of
- 01:20:35times behavior is still not greatly
- 01:20:37impacted or even almost normal if the
- 01:20:40other side of the brain is still functioning,
- 01:20:42so to avoid a possible outcome like that.
- 01:20:46For inhibition,
- 01:20:47people often will do it bilaterally and try
- 01:20:50to inhibit in both both sides of the brain.
- 01:20:53So it's it's.
- 01:20:54It's really just a.
- 01:20:55Experimental practicality
- 01:20:58thank you so much, sure.
- 01:21:03OK. Well, I I want to thank
- 01:21:07you again for a great lecture.
- 01:21:09Thank you all for being here
- 01:21:11and for your great questions
- 01:21:12and let's thank Dr Janik again.
- 01:21:15And for those of you who are
- 01:21:16having who are trainees who
- 01:21:18are having lunch with her,
- 01:21:19you should have the the link to
- 01:21:22the other zoom room and I hope
- 01:21:26you'll move over to that room to
- 01:21:29talk with Doctor Genich further.
- 01:21:31Thanks I wanna thank.
- 01:21:32I want to thank you all so much.
- 01:21:33This was really a pleasure and
- 01:21:35thanks for your great questions.