Postdoctoral Fellowship in Childhood Neuropsychiatric Disorders (T32) Trainee Talks

Transcript

• 00:00Good afternoon, everyone.
• 00:03It's my pleasure to introduce
• 00:05our, our grand rounds today.
• 00:07And, this is a very
• 00:08special grand rounds to me
• 00:10and to our center because
• 00:11we're presenting two t thirty
• 00:13two trainees.
• 00:14Before I do that, I
• 00:15just wanna give a shout
• 00:15out to, doctor Kareem Ibrahim.
• 00:18He'll be actually presenting next
• 00:19week on multimodal neuroimaging markers
• 00:21of transdiagnostic
• 00:22symptom domains in youth, the
• 00:24role of emotion regulation. And
• 00:25also, a shout out to
• 00:27him because he's also a
• 00:28t thirty two graduate.
• 00:30So, the first talk will
• 00:31be led by,
• 00:33Ellie Edgar,
• 00:35and her talk is the
• 00:36avoidant brain neurosignatures of risk
• 00:37avoidance in adolescence with a
• 00:39look toward early childhood measurement.
• 00:41So we she'll be talking
• 00:42about
• 00:43a preschool brat, but not
• 00:44the kind you think of.
• 00:46And, another shout out to
• 00:48her is that she's entertaining
• 00:49two job offers right now,
• 00:50academic job offers. So it's
• 00:52McDonald's or Burger King, we're
• 00:53thinking about. Yeah.
• 00:55And then secondly, I I
• 00:57wanna introduce
• 00:58doctor Dan Doyle, and he'll
• 01:00be speaking to us, another
• 01:01t thirty two graduate this
• 01:02year.
• 01:03He'll be sticking around to
• 01:04work on a k award
• 01:06with doctors Seston and Patty
• 01:09Bierman.
• 01:10And
• 01:11doctor Doyle, Dan's talk is
• 01:12deciphering genetic and circuit features
• 01:14of neurodevelopmental
• 01:15disorders.
• 01:16And lastly, I wanna give
• 01:17everyone a shout out online
• 01:18just to remind you that,
• 01:20our our own Trisha Doll
• 01:21has really ramped up our
• 01:22our treats here. So we
• 01:24have cheesecake and caramel brownies.
• 01:26I realize I'm creating a
• 01:27little bit of FOMO, and
• 01:28that's intentional. So we hope
• 01:29to see you at Grand
• 01:30Rounds in person in the
• 01:31future. Thank you.
• 01:40Awesome. Thank you for that
• 01:41introduction, doctor Crowley. So today,
• 01:43I'm gonna talk to you
• 01:44guys about my more recent
• 01:46research,
• 01:47here at the Courage Lab
• 01:48with doctor Crowley. So today,
• 01:49I'll be talking about neural
• 01:51signatures of risk avoidance
• 01:52in adolescents
• 01:54with a brief look towards
• 01:55early childhood measurement.
• 01:57But first, let's just picture
• 01:59this. So a teenager is
• 02:01standing at the edge of
• 02:02a diving board.
• 02:03Below them is deep water
• 02:05and around them are friends
• 02:07urging them to jump. Their
• 02:08hearts pounding, do they leap
• 02:10or do they take a
• 02:11step back?
• 02:12And so adolescence is often
• 02:13painted as a time of
• 02:14this reckless risk taking. Right?
• 02:17Driving too fast,
• 02:18breaking the rules, chasing a
• 02:20thrill. But there's also another
• 02:21side to this and that's
• 02:23avoidance.
• 02:24Just as some teens take
• 02:25that leap, others hesitate,
• 02:28hold back, and miss opportunities.
• 02:30And so what separates the
• 02:31risk takers
• 02:32from the avoiders?
• 02:34And how does each path
• 02:35shape their mental health and
• 02:36their future? And so today,
• 02:38I'll briefly discuss adolescent risk
• 02:40avoidance and why embracing risk
• 02:42can be just as consequential
• 02:43as embracing it.
• 02:46And so risk taking and
• 02:47risk avoidance enable individuals to
• 02:50adapt to a constantly evolving
• 02:52environment.
• 02:53These behaviors are complex reactions
• 02:55guided by both reflex and
• 02:57cognitive control.
• 02:59And so contexts that elicit
• 03:00both risk taking and avoidance
• 03:02behaviors are ambiguous,
• 03:04unpredictable, and or novel, and
• 03:06these contexts
• 03:08often include the possibility of
• 03:09both reward and loss, such
• 03:12that the relative
• 03:13safety benefits of avoiding avoiding
• 03:15the situation or stimuli must
• 03:17be weighed against the possibility
• 03:19of sacrificing
• 03:20potential rewards.
• 03:23Now all of the decisions
• 03:25we make have some degree
• 03:26of risk involved,
• 03:27and some degree of risk
• 03:28taking is necessary, right, to
• 03:30promote positive health outcomes,
• 03:32prepare for adulthood.
• 03:34But at the extremes,
• 03:36exaggerated risk taking can lead
• 03:37to negative consequences.
• 03:40At the other end of
• 03:41that spectrum,
• 03:42exaggerated
• 03:43risk avoidance is also associated
• 03:45with various types of dysfunction,
• 03:47and so both extremes lead
• 03:48to adverse outcomes.
• 03:51But exaggerated risk avoidance plays
• 03:53an important role in anxiety.
• 03:56So across all of the
• 03:57disorders under the anxiety umbrella
• 03:59in the DSM five, avoidance
• 04:01of stimuli or situations perceived
• 04:03as dangerous or threatening is
• 04:05a cardinal feature in the
• 04:07development and maintenance of anxiety.
• 04:09And so theories of cognitive
• 04:11behavior regard risk avoidance as
• 04:13a consequence of anxiety symptoms.
• 04:15For instance, the experience of
• 04:17anxiety can serve as a
• 04:18salient form of information to
• 04:20individuals,
• 04:21signaling the presence of threat
• 04:23in the environment.
• 04:24And so this anxiety manifested
• 04:26in part by attentional biases
• 04:28towards threat and increased error
• 04:30monitoring
• 04:31may potentiate risk avoidant decision
• 04:33making as a means towards
• 04:35avoiding perceived threats.
• 04:37Now it's also been proposed
• 04:39that risk avoidance is a
• 04:40cause and consequence of anxiety,
• 04:43and so this could suggest
• 04:44the presence of a self
• 04:45perpetuating
• 04:46cycle
• 04:47in which bias risk appraisals
• 04:49evoke anxiety,
• 04:51the experience of anxiety perpetuates
• 04:53the formation of negative risk
• 04:55appraisals,
• 04:57negative risk appraisals and anxiety
• 04:59both act as inputs into
• 05:00the decision making process, and
• 05:02risk avoidant decision making potentiates
• 05:04pervasive patterns of risk avoidant
• 05:06behavior.
• 05:08And so shifting gears slightly,
• 05:10we all know that EEG
• 05:11is a useful tool for
• 05:13delineating the neural underpinnings of
• 05:14a wide range of psychological
• 05:16processes.
• 05:17And so ERPs or event
• 05:18related potentials
• 05:20are phase and time locked
• 05:21neuroactivity
• 05:22created by averaging frequencies across
• 05:25multiple trials.
• 05:26And so within the context
• 05:27of risk, the FRN component
• 05:30of the N2 has received
• 05:31the most research focus.
• 05:33So the FRN is known
• 05:34to be sensitive to negative
• 05:36feedback, and we know that
• 05:37a greater amplitude is associated
• 05:39with risk taking in non
• 05:41anxious adults and adolescents.
• 05:43And further, it's been linked
• 05:45to anxiety in adult populations.
• 05:49Considerable research is also focused
• 05:51on the p three proposed
• 05:52to reflect attention orientation to
• 05:54unexpected events.
• 05:56And so we know a
• 05:57reduced amplitude is associated with
• 05:59risk taking in nonanxious adults
• 06:01and adolescents.
• 06:02It's also been linked to
• 06:03anxiety in both of these
• 06:04populations.
• 06:07But less work is focused
• 06:09on the p two, known
• 06:10to reflect sensory processes like
• 06:12attention following a feedback stimulus.
• 06:14And so what we do
• 06:15know is that a greater
• 06:16P2 amplitude is associated with
• 06:19higher levels of risk taking
• 06:20in non anxious adults.
• 06:23And finally, little work has
• 06:25assessed the LPP or slow
• 06:27wave
• 06:28proposed to reflect facilitated attention
• 06:30to emotionally or motivationally salient
• 06:32stimuli.
• 06:33And so a greater LPP
• 06:34amplitude
• 06:35is also associated with higher
• 06:37levels of risk taking, again,
• 06:39only demonstrated in non anxious
• 06:41adults.
• 06:43And so oscillatory
• 06:45dynamics complement ERPs in that
• 06:47they can be used to
• 06:47investigate phases of high or
• 06:49low excitability.
• 06:51One measure, event related spectral
• 06:53perturbation, or ERSPs,
• 06:56index the mean change in
• 06:57EEG power relative to baseline
• 06:59that is associated with the
• 07:01presentation of stimuli or execution
• 07:03of responses.
• 07:05And a common target for
• 07:06assessing the neural dynamics of
• 07:08uncertain or risky situations is
• 07:10theta band oscillatory power.
• 07:13So mid frontal theta reflects
• 07:15medial prefrontal cortex processes,
• 07:18which are highly sensitive to
• 07:19tasks involving novelty, conflict,
• 07:22punishment, and error, as well
• 07:24as feedback processing.
• 07:26Mid frontal theta has also
• 07:28been identified in the generation
• 07:30of event related mid frontal
• 07:31voltage negativities,
• 07:33like the n two, and
• 07:34we'll come back to that
• 07:34later.
• 07:36But theta power has been
• 07:37found to reflect individual differences
• 07:39in risk taking in adults.
• 07:41But only one study has
• 07:42linked it to anxiety in
• 07:43the context of risk, demonstrating
• 07:45that highly anxious
• 07:47adults showed amplified mid frontal
• 07:49theta power before making a
• 07:51less risky choice during a
• 07:52risk game.
• 07:55Now aside from gaps in
• 07:56the amount and types of
• 07:58ERP research and theta oscillatory
• 08:00dynamic research, if you didn't
• 08:02notice, all of these studies
• 08:04were in the context of
• 08:05risk taking,
• 08:06and that's a direct result
• 08:19performance,
• 08:20as well as avoidance motivation,
• 08:22so the potential for loss.
• 08:24And so this makes it
• 08:25difficult to determine if an
• 08:26individual shows risk avoidance or
• 08:28low approach motivation.
• 08:32And so Crowley et al
• 08:33addressed this gap by inverting
• 08:34the existing balloon analog risk
• 08:36task or BART.
• 08:38This new task, going to
• 08:39the balloon risk avoidance task,
• 08:41biases participants towards the perception
• 08:44of risk.
• 08:45And so each of thirty
• 08:46trials starts with the balloon
• 08:48being inflated next to the
• 08:49meter. And if you look
• 08:50at the meter, it ranges
• 08:51from that blue to red,
• 08:52so it's safe to more
• 08:54dangerous as you go up.
• 08:56And during each trial, an
• 08:57eight second timer imposes a
• 08:59mild time pressure, and this
• 09:01is for the participant to
• 09:02decide how much to deflate
• 09:04each balloon.
• 09:05If no action is taken,
• 09:06the balloon will pop.
• 09:09Now when the balloon is
• 09:10initially inflated, it's worth the
• 09:12full value of one hundred
• 09:13and ten points.
• 09:15Participants give up points to
• 09:16deflate the balloon,
• 09:18reducing the risk or likelihood
• 09:19that it will pop, but
• 09:21also decreasing the balloon's value
• 09:22and in turn the amount
• 09:23of points that they can
• 09:24earn for that trial.
• 09:27And so the more air
• 09:28that they release from the
• 09:29balloon, the safer the balloon
• 09:31is, but the point value
• 09:33of the trial is lower
• 09:34as indicated up top.
• 09:36The less air that is
• 09:37released, the higher the likelihood
• 09:39it will pop, but the
• 09:40point value of the trial
• 09:41is higher as indicated on
• 09:43the bottom of the screen.
• 09:46And so if the balloon
• 09:47does not pop, we call
• 09:48this successful avoidance.
• 09:50And if the balloon pops,
• 09:51we call this unsuccessful avoidance.
• 09:54And so in their initial
• 09:55study, Crowley and colleagues demonstrated
• 09:57that risk avoidant behavior on
• 09:59the BRAT was positively related
• 10:01to overall adolescent anxiety and
• 10:04fearful temperament.
• 10:05Moreover, the BART, which measures
• 10:07both risk avoidance and approach
• 10:09motivation, like I mentioned earlier,
• 10:11was unrelated to anxiety in
• 10:12their study, highlighting the utility
• 10:14of the BRAT for studying
• 10:16risk avoidance specifically.
• 10:19And so given these promising
• 10:20behavioral results, the first study
• 10:22I will discuss today builds
• 10:23directly on these findings by
• 10:25examining the neural dynamics of
• 10:27youth risk avoidance during the
• 10:28BRAT.
• 10:30And so our sample consisted
• 10:31of fifty nine adolescents eleven
• 10:33to nineteen years of age.
• 10:35The BRAT was used with
• 10:36concurrent EEG to measure ERPs
• 10:38and theta oscillatory dynamics
• 10:40in response to unsuccessful
• 10:42and successful risk avoidance conditions.
• 10:45And the social phobia and
• 10:46anxiety inventory for children was
• 10:48used to drive groups of
• 10:50high and low social anxiety.
• 10:53Alright. Now first, youth reporting
• 10:56high levels of social anxiety
• 10:58exhibited larger p two,
• 11:00LPP,
• 11:01and FRN amplitudes to unsuccessful
• 11:04compared to successful risk avoidance.
• 11:07However, there is no significant
• 11:09difference in P3 amplitude
• 11:11between successful
• 11:12and unsuccessful avoidance as a
• 11:14function of social anxiety level.
• 11:16But across the whole sample
• 11:18of youth,
• 11:19youth show smaller p three
• 11:20responses to unsuccessful
• 11:22relative to successful avoidance.
• 11:26And so second, youth with
• 11:27higher levels of social anxiety
• 11:29showed smaller theta responses
• 11:31following successful avoidance
• 11:33compared to those with low
• 11:34levels of social anxiety.
• 11:38And that same exact effect
• 11:39was found for unsuccessful avoidance.
• 11:42Those with higher levels of
• 11:43social anxiety showed smaller theta
• 11:46power responses than those with
• 11:47lower levels of social anxiety.
• 11:52And so first, the p
• 11:53two, FRN, and LPP, but
• 11:56not p three, for unsuccessful
• 11:58avoidance significantly
• 11:59differentiated high and low social
• 12:01anxiety groups.
• 12:02So regarding the p two,
• 12:04overall, youth with greater levels
• 12:06of social anxiety may have
• 12:07expected to avoid losing,
• 12:09and this was indeed reflected
• 12:10in our behavioral data. So
• 12:12they gave up more points
• 12:14and took less risk than
• 12:15those with lower levels of
• 12:16social anxiety.
• 12:17And so, in turn, this
• 12:18risk avoidance may have been
• 12:19reflected in that P two
• 12:20amplitude.
• 12:22And we know the FRN
• 12:23is sensitive to negative feedback.
• 12:25So given that youth with
• 12:26high levels of social anxiety
• 12:28tended to avoid risk, they
• 12:30might have been surprised when
• 12:31they let more air out
• 12:32of the balloon, a safer
• 12:33balloon, but it still popped,
• 12:35reflected by a larger FRN
• 12:37response.
• 12:40The larger LPP amplitude in
• 12:42youth with social anxiety might
• 12:43indicate greater sustained attention to
• 12:46unsuccessful risk avoidance,
• 12:48supporting the view that individuals
• 12:49with social anxiety
• 12:51might exhibit attentional negativity biases
• 12:53to aversive stimuli.
• 12:56And regarding the P3, we
• 12:57didn't find the effect as
• 12:59a function of social anxiety,
• 13:00and so we might have
• 13:01found a smaller amplitude for
• 13:03unsuccessful
• 13:04relative to successful avoidance across
• 13:06the whole sample
• 13:07because of the loss of
• 13:09points for those with low
• 13:10levels of social anxiety
• 13:12and the loss of avoiding
• 13:13risk for those with high
• 13:14levels of social anxiety. And
• 13:16so for both groups, some
• 13:18type of loss was reflected
• 13:19in the reduced P three.
• 13:22Now second, higher levels of
• 13:24social anxiety were associated with
• 13:25mid frontal theta for successful
• 13:27and unsuccessful avoidance.
• 13:30And in youth with higher
• 13:31levels of social anxiety, we
• 13:33found this reduced mid frontal
• 13:35theta response in conjunction with
• 13:37an increased FRN response.
• 13:40Both responses, though, differentiated risk
• 13:42avoidance in youth with social
• 13:44anxiety,
• 13:45albeit in the opposite direction,
• 13:47highlighting the importance of contrasting
• 13:49oscillatory dynamics
• 13:51and ERPs in order to
• 13:52develop a more comprehensive
• 13:54explanation and potential biomarker for
• 13:56risk avoidance
• 13:57in socially anxious youth.
• 14:01And so now thinking back
• 14:02to the beginning of this
• 14:03presentation,
• 14:04we know that adolescence is
• 14:05that time of risk taking
• 14:07and reckless impulsivity,
• 14:08and these characteristics are very
• 14:10often associated with teen alcohol
• 14:12use. But what about risk
• 14:14avoidance and alcohol use? And
• 14:16so in the second study
• 14:17I will discuss today, we
• 14:18assessed risk avoidance, theta, and
• 14:21anxiety sensitivity as predictors of
• 14:23age of adolescent alcohol initiation.
• 14:26So a new sample of
• 14:28one hundred seventeen youth, thirteen
• 14:29to seventeen years of age,
• 14:31participated.
• 14:32They received the BRAT with
• 14:33concurrent EEG to assess mid
• 14:35frontal theta and the revised
• 14:37childhood anxiety sensitivity index to
• 14:40assess anxiety sensitivity.
• 14:43And so first, youth showed
• 14:45significantly higher mid frontal theta
• 14:47power for unsuccessful
• 14:48compared to successful avoidants.
• 14:51And that's demonstrated
• 14:52in this picture
• 14:55as well as in this
• 14:56one.
• 15:01And so second, lower mid
• 15:03frontal theta power following unsuccessful
• 15:05avoidance
• 15:06was associated with greater anxiety
• 15:08sensitivity,
• 15:09but this relation was not
• 15:10evident for successful avoidance.
• 15:15And finally, we found that
• 15:16mid frontal theta power following
• 15:18unsuccessful avoidance
• 15:20moderated the relation between anxiety
• 15:22sensitivity
• 15:23and alcohol initiation age.
• 15:25So those with high levels
• 15:27of mid frontal theta following
• 15:28unsuccessful avoidance
• 15:30showed a decrease in alcohol
• 15:31initiation age as their CASI
• 15:33scores increased.
• 15:34So the more anxiety sensitive,
• 15:36the younger the age of
• 15:38alcohol initiation.
• 15:40Conversely, youth with low levels
• 15:42of mid frontal theta following
• 15:43unsuccessful avoidance
• 15:45showed an increase in alcohol
• 15:46initiation age as CASI scores
• 15:48increased. So the more anxiety
• 15:50sensitive here, the older the
• 15:52age of alcohol initiation.
• 15:57And so consistent with the
• 15:58non anxious subsample in study
• 16:00one, adolescents exhibited higher mid
• 16:03frontal theta power following unsuccessful
• 16:05compared to successful avoidance on
• 16:07the BRAT.
• 16:09Also consistent with the socially
• 16:11anxious subsample in study one,
• 16:13lower mid frontal theta following
• 16:15unsuccessful avoidance was associated with
• 16:17greater anxiety sensitivity.
• 16:20Now adolescents categorized as as
• 16:22exhibiting high theta power tended
• 16:24to initiate alcohol at earlier
• 16:27ages as their anxiety sensitivity
• 16:29scores increased.
• 16:30Considering this finding in conjunction
• 16:32with the motivational drinking model,
• 16:34it's possible that high anxiety
• 16:36sensitive individuals
• 16:38might initiate drinking at a
• 16:39younger age to cope with
• 16:41their elevated anxiety symptoms
• 16:43because it's challenging to shift
• 16:45to a different, perhaps more
• 16:46positive activity once presented with
• 16:48alcohol
• 16:49or a combination of both.
• 16:51So if they're drinking to
• 16:52cope with their anxiety sensitivity
• 16:53and are successful,
• 16:55negative reinforcement supports continuation of
• 16:57alcohol use.
• 17:00Now, conversely, low theta category
• 17:02adolescents
• 17:03tended to initiate at later
• 17:04ages as their anxiety sensitivity
• 17:06scores increased.
• 17:08And so decreased theta power
• 17:10might reflect an adolescent's ability
• 17:11to shift attention
• 17:13from alcohol and its consequences
• 17:15to other goals and activities.
• 17:17And so adolescents with greater
• 17:18anxiety sensitivity
• 17:20would initiate alcohol use at
• 17:21a later age because they're
• 17:23typically more risk avoidant than
• 17:24their less anxiously
• 17:26sensitive counterparts.
• 17:29And so shifting gears just
• 17:30one more time, we'll talk
• 17:31a little bit about current
• 17:32directions. So what I'm doing
• 17:33right now is measuring risk
• 17:35avoidance in early childhood.
• 17:38And so critically many children
• 17:40show elevated anxiety and a
• 17:41proclivity to avoid novelty and
• 17:43risk very early in life.
• 17:45And a good number of
• 17:46these children might fail to
• 17:47actualize their potential by not
• 17:49taking risks interpersonally,
• 17:51in the classroom, or on
• 17:52the sports field. And so
• 17:54imagine if we could identify
• 17:56a risk avoidance proclivity
• 17:57earlier in life.
• 18:00But to date, no studies
• 18:01have assessed risk avoidance in
• 18:02preschool age children,
• 18:05and only two studies have
• 18:06assessed risk taking.
• 18:07And so the first study,
• 18:09consistent with work in adolescents
• 18:10and adults, shows that highly
• 18:12exuberant preschoolers, so characterized by
• 18:15more positive reactivity
• 18:17to novelty, greater approach behavior,
• 18:19sociability,
• 18:20they had a greater propensity
• 18:21for risk taking at five
• 18:22years of age.
• 18:25And not a surprise, the
• 18:26second study found that the
• 18:27anxiety related constructs behavioral inhibition,
• 18:30so characterized by vigilance towards
• 18:32novelty, heightened negative act affect,
• 18:35withdrawal from unfamiliar
• 18:37social situations
• 18:38was not associated with risk
• 18:40taking in children at four
• 18:41years of age.
• 18:42And so although these studies
• 18:44provide an important
• 18:45foundation for risk taking in
• 18:47children, crucial knowledge gaps remain
• 18:49regarding risk avoidance.
• 18:51And so setting the foundation
• 18:53for future work on developmental
• 18:54risk for anxiety,
• 18:55I received the Yale Child
• 18:57Study Center Trainee Pilot Research
• 18:59Grant to examine two aims
• 19:01involving the behavioral and neural
• 19:03indices of risk avoidance in
• 19:04relation to symptoms of anxiety
• 19:06and temperament in preschool aged
• 19:08children.
• 19:10And so this is what
• 19:11it looks like. So I
• 19:12slightly modified the BRAT by
• 19:13first changing the value of
• 19:14the balloons
• 19:15from numerical points
• 19:17to candies. And second, by
• 19:19exchanging the total point
• 19:20count with a jar that's
• 19:21filled with candies. And so
• 19:23the preschool BRAT also features
• 19:25more training, like, before the
• 19:26task in order for children
• 19:28to fully grasp the task
• 19:29at hand. And so data
• 19:31collection for this is currently
• 19:32ongoing,
• 19:33but I look forward to
• 19:34analyzing and writing it up
• 19:35in a manuscript as well
• 19:37as building on them in
• 19:38a future grant application.
• 19:41And so that's all we
• 19:42have time for today, and
• 19:43so I'd like to thank
• 19:43you all for listening and
• 19:45for your attention.
• 19:59Just turning it on. Here
• 20:00we go.
• 20:03Can you hear me? Okay.
• 20:05Questions.
• 20:06You have five minutes.
• 20:12Thank you. I just wanted
• 20:13to ask you about how
• 20:14how you determined alcohol initiation
• 20:17in adolescence. So was it
• 20:18just the first time they
• 20:19ever tried alcohol, or was
• 20:21it that they were using
• 20:22it
• 20:22more consistently? How how was
• 20:24that categorized? Oh, yeah. In
• 20:25this study, it was categorized
• 20:26as their first use.
• 20:33Thank you for a great
• 20:34presentation.
• 20:36Can you comment a little
• 20:37bit about the, sample of
• 20:39participants? Is it community volunteers
• 20:41or clinical sample? And if
• 20:42there was any,
• 20:44medication that they would take
• 20:46in a co occurring disorders
• 20:47that were recorded?
• 20:49Yeah. So both of these
• 20:51were community
• 20:52based
• 20:53samples. So we recruited by,
• 20:55like, a mass mail out.
• 21:03Thank you, Ellie. That was
• 21:03an incredibly clear presentation. And
• 21:05either of the institutions that
• 21:07have given you a job
• 21:07offer will be lucky to
• 21:08have you.
• 21:10I so, in terms of
• 21:11I think you're you're getting
• 21:12to this in the last
• 21:13pilot study. Is there what's
• 21:15known about developmental change in
• 21:17theta power and the oscillatory
• 21:19dynamics that you mentioned that
• 21:20in your introduction? Does that
• 21:22show developmental change from childhood
• 21:24to adolescence?
• 21:25That's a very good question,
• 21:27and I have not yet
• 21:28looked into that. But that's
• 21:29something I would like to
• 21:30look into in the future
• 21:31as I connect these
• 21:33different
• 21:34areas of development. And it
• 21:36sounds like that data that
• 21:37you'll generate in your last
• 21:38study will help you do
• 21:39that. Yeah.
• 21:42Anyone
• 21:43else? Well, thank you, Ellie.
• 21:45Nice job. Thank you.
• 21:51Without further ado, doctor DeAndoio.
• 22:10Okay. So I'm gonna change
• 22:12gears pretty drastically.
• 22:15I'm a postdoc in Kathak
• 22:17Patapi Raman in the labs,
• 22:19and so we don't
• 22:20really see patients. We don't
• 22:22do a lot with human
• 22:23work. But I wanna point
• 22:24out at the beginning that
• 22:25Ellie brought up these different
• 22:27circuits or
• 22:28kind of connectivity responses that
• 22:30are important
• 22:32for risk avoidance or,
• 22:34alcohol initiation, these different factors.
• 22:37And we wanna understand how
• 22:38the development of those circuits
• 22:40occurs kind of throughout
• 22:43throughout life as well as
• 22:44especially during early development. And
• 22:46so
• 22:47not only how do they
• 22:48function, but how do they
• 22:49form in the first place?
• 22:51And so
• 22:53in Katak and Anad's labs,
• 22:55we're interested in the development
• 22:56of the cerebral cortex. Right?
• 22:58This is the outermost part
• 22:59of the brain, kind of
• 23:00the bark, and it's essential
• 23:02for our conscious thoughts, actions,
• 23:03and perceptions.
• 23:05And this cortex is a
• 23:06laminated structure. It's conveniently split
• 23:08into six individual layers
• 23:10with unique molecular identities and
• 23:12unique connectivities.
• 23:14So the upper layers, just
• 23:16layers two through four,
• 23:17send projections largely within the
• 23:19cortex.
• 23:20They could be within the
• 23:21same hemisphere or across the
• 23:22midline through structures like the
• 23:23corpus callosum
• 23:25to help integrate and process
• 23:26information.
• 23:27And by contrast, the deep
• 23:29layers here in red and
• 23:30green, layers five and six,
• 23:32send their projections largely out
• 23:34of the cortex to subcortical
• 23:35structures in the thalamus or
• 23:37in the spinal cord to
• 23:38help control movement.
• 23:40And these layers are situated
• 23:42right in the middle of
• 23:42the cortex between
• 23:44the deep most part, the
• 23:45subplate, and the top most
• 23:47part, the marginal zone or
• 23:49layer one.
• 23:50And despite these diverse neuronal
• 23:53identities and projection types,
• 23:55these neurons are all actually
• 23:56generated from the same pool
• 23:57of neural progenitor cells.
• 23:59This happens in a sequential
• 24:01inside out manner generally and
• 24:03begins with the deep layers,
• 24:04subplate six five, and then
• 24:06continues to the upper layers
• 24:08four two three
• 24:09four three two.
• 24:11And so,
• 24:12generally, our labs are interested
• 24:14in three main questions. First,
• 24:16and one that I'll not
• 24:17really focus on today, is
• 24:19how are all of these
• 24:20distinct neuronal types are generated
• 24:22from the same pool of
• 24:23progenitors?
• 24:24Second, how they ultimately wire
• 24:26together to form brain circuits
• 24:27and how they end up
• 24:28being functional in the long
• 24:29run.
• 24:30And then third, what aspects
• 24:32of these processes are altered
• 24:34in humans, and how can
• 24:35they contribute to neurodevelopmental
• 24:37disorders.
• 24:39And so
• 24:41oh,
• 24:44so here we can see
• 24:45kind of an example of
• 24:45their different molecular identities.
• 24:47Just like the schematic, you
• 24:49can see the subplate here
• 24:50in purple, layer six in
• 24:52green,
• 24:53layer five in red, two
• 24:54through four in blue, and
• 24:55then one in kind of
• 24:56brown.
• 24:59But I first wanna highlight
• 25:00a couple examples of why
• 25:01this is relevant for understanding
• 25:03the human condition. So we
• 25:04focus on a layer six
• 25:05marker, TBR one, here in
• 25:07green.
• 25:08Its role in brain development
• 25:09is pretty well studied over
• 25:11the past twenty or so
• 25:12years,
• 25:14both here at Yale and
• 25:15outside.
• 25:16And so mutations in TBR
• 25:18one are associated with intellectual
• 25:20developmental disorder and autism with
• 25:22speech delay.
• 25:23And here you can see
• 25:24two different t one weighted
• 25:26MRIs
• 25:27from different patients, both of
• 25:28whom have frontal pachygyria.
• 25:32And while we can see
• 25:33these alterations in human data,
• 25:35it can be really difficult
• 25:36to kind of get a
• 25:37deeper understanding of what's going
• 25:39on, what causes these different
• 25:40phenotypes.
• 25:42And so
• 25:43to get at that, we
• 25:44turn to mice.
• 25:46So luckily, we have exceptional
• 25:48mouse genetic tools that can
• 25:49help us understand the different
• 25:50molecular bases of typical and
• 25:52divergent brain development.
• 25:54And so here in the
• 25:55bottom, this is now a
• 25:56mouse brain. So
• 25:58mice have smooth brains. They
• 26:00don't have psoas or gyri.
• 26:02And here in green, you
• 26:03can see a coronal section
• 26:05in which we have projections
• 26:06from the thumb from the
• 26:07cortex
• 26:08up around into the thalamus,
• 26:10and a sagittal section where
• 26:11we don't yet have much
• 26:12innervation
• 26:14down into the midbrain and
• 26:15through the cerebral peduncles.
• 26:17And, normally, this is what
• 26:18we would see at this
• 26:19stage.
• 26:20But if we were to
• 26:21take away TBR one from
• 26:22these mice, you can see
• 26:23on the bottom,
• 26:25the connections are altered drastically.
• 26:27So instead of projections going
• 26:29from the cortex to the
• 26:30thalamus,
• 26:31they dive more ventrally towards
• 26:33the hypothalamus.
• 26:34And there's also exuberant outgrowth
• 26:36of these projections into the
• 26:37midbrain and cerebral peduncles.
• 26:40And so using these tools,
• 26:42we're able to get a
• 26:42better understanding of how developing
• 26:44circuits are altered following gene
• 26:46mutations seen in patients.
• 26:48We're kind of able to
• 26:49fine tune this and say,
• 26:50okay, in this scenario, we
• 26:52took out the entirety of
• 26:53the TBR one gene. This
• 26:54is not always the case
• 26:56in human patients. They may
• 26:57have a frame shift or
• 26:58a partially functional protein.
• 27:00And so we're able to
• 27:02adjust our mice to get
• 27:03a sense of those different
• 27:04changes as well. And for
• 27:06TBR one, for example, this
• 27:07has been done by
• 27:09Brian O'Rourke and Kevin Wright's
• 27:10groups in Oregon,
• 27:12to study different patient mutations
• 27:14of TBR one to look
• 27:16at circuit connectivity.
• 27:19And then one of the
• 27:20most obvious and drastic cases
• 27:21in which studying these genes
• 27:23has been informative is mutations
• 27:24in the Rheeland gene. So
• 27:26Rheeland is expressed largely in
• 27:28layer one marginal zone neurons,
• 27:30but mutations in this gene
• 27:31are associated with Norman Roberts
• 27:33syndrome, which also has lysencephaly.
• 27:35You can see pretty much
• 27:37smooth brain,
• 27:39and altered lamination of the
• 27:41cortex. So the layers are
• 27:42actually in the opposite order
• 27:43that we would expect.
• 27:45But at first, our understanding
• 27:47of the real end function
• 27:49might be hampered by using
• 27:50mice because they're normally lysencephalic.
• 27:52So lysencephaly is not a
• 27:54phenotype that we can observe
• 27:55in them.
• 27:57However, if we look at
• 27:58DTI data from a control
• 28:00mouse here on the left,
• 28:02you can see thalamocortical
• 28:03projections here in green. They
• 28:05enter the cortex, then they
• 28:07kind of organize into these
• 28:09discrete structures.
• 28:10By contrast,
• 28:12if we
• 28:13delete the real engine,
• 28:15alter the cortical lamination,
• 28:16these thalamocortical
• 28:17projections no longer organize into
• 28:19their discrete structures.
• 28:23And these examples kind of
• 28:24illustrate that we can effectively
• 28:26use mice to get a
• 28:27much deeper understanding of circuit
• 28:28development and the alterations that
• 28:30may occur in brain disorders.
• 28:32But thalamocortical connectivity is a
• 28:34great example for that because
• 28:36as these signals are sent
• 28:37from the thalamus to the
• 28:38cortex, they're often important for
• 28:39sensory processing and often altered
• 28:41in neurodevelopmental disorders.
• 28:43So seeing how they're really
• 28:45changing can help us get
• 28:46a better sense of of
• 28:47the connections that we may
• 28:48be seeing, and maybe it's
• 28:50EEG or something else.
• 28:53And so thalamic cortical projections
• 28:55follow a very stereotype developmental
• 28:57trajectory. These ages at the
• 28:59top, you can ignore unless
• 29:01you wanna know about mouse
• 29:02ages. But,
• 29:05initially, they grow out of
• 29:06the thalamus here in magenta
• 29:07and into the developing striatum.
• 29:10They then reach into the
• 29:11cortex, and they wait there,
• 29:12and they kind of they
• 29:13go through what's called a
• 29:14waiting period. It's been really
• 29:15well described,
• 29:17for the last
• 29:19thirty to forty years,
• 29:21while they wait for their
• 29:22layer four targets to be
• 29:23born and migrate. So they
• 29:24have nothing to connect with
• 29:26at this point.
• 29:27They later grow into the
• 29:28cortex and then finally they
• 29:30organize into discrete structures. So
• 29:32in the mouse, an example
• 29:33of this are the whisker
• 29:34barrels.
• 29:35This organization corresponds to the
• 29:37sensory information from each of
• 29:38the mouse's whiskers. So we
• 29:40know exactly what location corresponds
• 29:42to which whisker on their
• 29:43face.
• 29:44But
• 29:45why do we care so
• 29:46much about thalamocortical
• 29:47connections?
• 29:49And so the thalamus sends
• 29:50projections to a variety of
• 29:52cortical regions, including the prefrontal
• 29:53cortex that Ali brought up,
• 29:56somatomotor regions, and more.
• 29:58And these connections are altered,
• 29:59both increase and decrease in
• 30:01various brain disorders. For example,
• 30:03schizophrenia and bipolar
• 30:05have
• 30:06reduced or increased connectivity depending
• 30:09on which areas you look
• 30:10at. The same for major
• 30:11depressive disorder. And autism also
• 30:13has reports of increased thalamus
• 30:15to temporal and thalamus to
• 30:16somatic motor and parietal connectivity
• 30:18patterns.
• 30:19And so
• 30:21we see all these reports
• 30:23of changes, but the hard
• 30:25part is how do these
• 30:26projections and connections develop in
• 30:27the first place? So are
• 30:29they getting set up the
• 30:30wrong way to begin with?
• 30:31But and at that level,
• 30:33what are the molecular components
• 30:35kind of regulating that?
• 30:37And so
• 30:38so what I'll talk a
• 30:39little bit about today,
• 30:42and this is an ongoing
• 30:43project in the lab and
• 30:45along with another lab in
• 30:47Germany. And so
• 30:49we really wanna get a
• 30:50complete understanding of how these
• 30:51circuits come to form during
• 30:52development and then to map
• 30:54this assembly and how they
• 30:56refine
• 30:57over time. So this is
• 30:58collaboratively
• 30:59done with Rachel Bandler, another
• 31:01great postdoc in the Pattabhi
• 31:02Raman and Sustin Labs, who's
• 31:05also in the NRTP.
• 31:07And then Connor Lynch, who's
• 31:08a very talented
• 31:10oh, no.
• 31:11A very talented grad student
• 31:13in Christian Meyer's lab in
• 31:14Germany.
• 31:15And so
• 31:17traditionally, we would use immuno
• 31:18staining on sections to assess
• 31:20projections. And so here is
• 31:21another mouse brain. In green,
• 31:23you can see all the
• 31:24projections from the thalamus to
• 31:25the cortex.
• 31:26Here's an example of those
• 31:27whisker barrel structures in practice.
• 31:32But this restricts what we
• 31:33can truly assess. And so
• 31:34to bypass this limitation in
• 31:36the lab, we've implemented whole
• 31:38brain clearing and imaging
• 31:40so we can investigate entire
• 31:41brains without sectioning
• 31:43or artifacts or different staining
• 31:45issues. And so,
• 31:48as an example here, you
• 31:49can see this is an
• 31:50entire mouse brain,
• 31:52with all the layer five
• 31:53neurons in red.
• 31:55And so
• 31:56as we
• 31:57spin it, we can see
• 31:58all the projections coming from
• 32:00those
• 32:01those neurons as well, such
• 32:03as
• 32:05through the internal capsule, down
• 32:07past the ponds, and then
• 32:08the pyramidal decussation.
• 32:11So we're able to assess
• 32:12entire circuits,
• 32:14but
• 32:15these circuits have multiple components.
• 32:17What connects with what?
• 32:19So how do we label
• 32:20true connections between cells?
• 32:23So to get at this
• 32:24question, we take an advantage
• 32:25of the rabies virus.
• 32:29And so the rabies virus
• 32:30was first used for tracing
• 32:32studies in the late nineteen
• 32:33eighties,
• 32:35but it has become more
• 32:36prominent and optimized over the
• 32:37last twenty years or so.
• 32:39And so what makes the
• 32:40rabies virus special is that
• 32:41by modifying it, we're able
• 32:43to control what cells are
• 32:44able to take up the
• 32:45virus initially. We would call
• 32:47those starter cells. And second,
• 32:49it travels retrogradely, so back
• 32:51from a projection
• 32:53via a synapse. So we're
• 32:55able to identify what monosynaptically
• 32:57connects to our starter cells.
• 32:58So what forms a functional
• 32:59circuit with the cell we
• 33:01initially infected?
• 33:03So in this scenario,
• 33:06here, our red cell is
• 33:07any cell that is able
• 33:08to be a starter cell.
• 33:09It's able to take up
• 33:10the rabies virus.
• 33:12A yellow cell is
• 33:14a starter cell that has
• 33:16taken up the rabies virus,
• 33:17so it's both red and
• 33:18green.
• 33:19And then anything that connects
• 33:20to that starter cell is
• 33:22only green. It has the
• 33:23rabies virus, but not the
• 33:24ability to be a starter
• 33:26cell.
• 33:28So basically, we know which
• 33:30neurons form synaptic connections with
• 33:32each other.
• 33:33And so once we got
• 33:34this tool kind of working
• 33:35within the lab, we utilized
• 33:37it in the context of
• 33:38the frontal cortex. We think
• 33:39this is a really important
• 33:40region. The prefrontal cortex is
• 33:42greatly expanded in humans compared
• 33:44to other primates.
• 33:45And so let's get a
• 33:46sense of how the initial
• 33:47circuits are made even
• 33:49prior to human evolution.
• 33:52And so, again, this is
• 33:54mouse genetics on your left.
• 33:56I'm not gonna go into
• 33:57detail about it.
• 33:59But
• 34:00based on the
• 34:02the approach we took,
• 34:04all excitatory neurons within the
• 34:05cortex are potential starter cells.
• 34:09But we inject the rabies
• 34:11virus directly into the frontal
• 34:12cortex, and so here is
• 34:14a cleared brain on your
• 34:15right.
• 34:16And you can see the
• 34:17injection site here in kind
• 34:18of this magenta
• 34:20screen.
• 34:21And so we perform these
• 34:22experiments in mice right after
• 34:24birth, so the day they
• 34:25were born, and then we
• 34:26wait a week. This is
• 34:27relatively similar to late mid
• 34:29fetal stage for humans. So
• 34:31really
• 34:32what's happening during development.
• 34:35And so
• 34:36as a reminder, again, the
• 34:38magenta cells are potential starter
• 34:40cells and their projections.
• 34:41You can see we hit
• 34:42part of motor cortex as
• 34:43the corticospinal tract is labeled
• 34:45here as well,
• 34:46whereas the green cells are
• 34:48the inputs to those starters.
• 34:50So what made a functional
• 34:51connection with them within the
• 34:53first postnatal week of a
• 34:54mouse's life.
• 34:56And so taking a closer
• 34:58look at the three d
• 34:59image,
• 35:00maybe,
• 35:01you can see we get
• 35:02robust green labeling, especially in
• 35:04these
• 35:05subcortical
• 35:06structures.
• 35:07Here, where the laser pointer
• 35:08is is the thalamus.
• 35:10And so we can kind
• 35:11of go through our entire
• 35:13brain and follow every single
• 35:14neuron that's traced.
• 35:17And you saw the bright
• 35:18green there.
• 35:20And so if we take
• 35:20just a snippet of that,
• 35:23this is the mouse's thymus,
• 35:26and here's cortex. This is
• 35:27the injection side, and we
• 35:29can see that we get
• 35:30really robust labeling of medial
• 35:32dorsal,
• 35:33VPM, VPL,
• 35:34and
• 35:35VM nuclei of the thalamus.
• 35:37So during the early development
• 35:39of the mouse,
• 35:40the thalamus is making robust
• 35:42connections from these distinct nuclei
• 35:44to the frontal cortex.
• 35:46And so
• 35:47how do we make sense
• 35:48of these different cells? Right?
• 35:51Do we care what cell
• 35:52synapse down to what or
• 35:53just region to region?
• 35:54And so in addition to
• 35:56imaging, we're able to get
• 35:57the gene expression profiles for
• 35:58each of these green cells
• 35:59with single cell RNA seq.
• 36:02And so by taking those
• 36:03green cells and sequencing them,
• 36:05we're able to get this
• 36:06UMAP. So this is a
• 36:07two d representation
• 36:09of all the green cells
• 36:10that we've sequenced from one
• 36:11mouse brain, so any cell
• 36:13that had the rabies virus.
• 36:15Each dot is a cell.
• 36:17And based off the gene
• 36:18expression profiles, we identify what
• 36:21putative cell type they are,
• 36:22what we think they are.
• 36:24And what's really exciting is
• 36:25that we see green cells
• 36:26representing
• 36:27thalamocortical
• 36:28connections. So right here, these
• 36:30are all thalamic neurons that
• 36:32must have projected to the
• 36:33frontal cortex.
• 36:35But we also see a
• 36:36variety of other cortical cell
• 36:37types which represent shorter range
• 36:39connections. So those would be,
• 36:40for example, up here,
• 36:42excitatory cortex,
• 36:44excitatory neurons in the cortex.
• 36:45We also have some inhibitory
• 36:47neurons.
• 36:48And so we're really able
• 36:50to truly capture widespread dynamics
• 36:52of early connectivity.
• 36:53And I just wanna highlight
• 36:55kind of our confirmation of
• 36:56two smaller cortical neuron populations.
• 36:59So it'd be these. So
• 37:00first,
• 37:03the the yellow cell type
• 37:04here, we've labeled as layer
• 37:06four cortical neurons,
• 37:08and here would be deep
• 37:09layer cortical neurons.
• 37:11So if we were to
• 37:12look at our section, we
• 37:13can see that we see
• 37:14green within the cortex up
• 37:16here.
• 37:17And if we take the
• 37:18gene expression profile of the
• 37:20neurons that we sequenced, we're
• 37:21able to intersect it with
• 37:22existing spatial transcriptomic data from
• 37:24the Allen Institute. This is
• 37:26Mirfisch where they kind of
• 37:27get the gene expression profiles
• 37:28within space
• 37:30of numerous neurons.
• 37:32And so that helps us
• 37:33identify what the most likely
• 37:35location of our neurons are.
• 37:37And so if you look
• 37:38here, this is the spatial
• 37:39mapping. All the blue dots
• 37:40is where we expect
• 37:42the cells or where they
• 37:44potentially would be.
• 37:45And if we look just
• 37:46at a representative staining, I
• 37:48showed this in one of
• 37:49the first couple slides, this
• 37:50is consistent with those neurons
• 37:51being within the upper layers.
• 37:55In addition, cluster fourteen, which
• 37:57was that purplish cluster,
• 38:00seems to be only in
• 38:01the deepest parts of the
• 38:02cortex right here. This is
• 38:04a little bit of layer
• 38:05six a and mostly layer
• 38:07six b. So
• 38:10this is what the mouse
• 38:11cortex looks like at that
• 38:12point, and this purple would
• 38:13be those cells that we
• 38:14see.
• 38:15And so
• 38:17one reason we find this
• 38:18really cool is that layer
• 38:19six b is a remnant
• 38:21of the subplate, which is
• 38:22a transient zone during development,
• 38:24and we find this
• 38:25really cool because subplate neurons
• 38:27sit in the subplate beneath
• 38:28the cortical plate at the
• 38:30interface of cortical gray and
• 38:32white matter. So it sits
• 38:33between the rest of the
• 38:34neurons
• 38:35and all the projections that
• 38:36are growing, extending everything else.
• 38:38And so here on the
• 38:39left, you can see an
• 38:40example of a subplate neuron
• 38:42in green and typical pyramidal
• 38:44neurons in magenta. They're actually
• 38:46pretty distinct in their morphology,
• 38:47and they're really widespread in
• 38:49what they do.
• 38:50But
• 38:51what we think is so
• 38:52cool about this group and
• 38:53why we think it's interesting
• 38:54that we've traced them from
• 38:55early
• 38:56frontal cortex connections is that
• 38:58they're the firstborn neurons from
• 39:00the dorsal cortex,
• 39:01and they perform kind of
• 39:03diverse function throughout development.
• 39:05They make some of the
• 39:05earliest synapses. So, okay, it
• 39:07makes sense. We see some
• 39:08tracing very early on. But
• 39:10typically, this is not,
• 39:12that well studied in the
• 39:13context of frontal cortex.
• 39:16Additionally, they're some of the
• 39:17first to have activity,
• 39:19and they perform many non
• 39:21cell autonomous
• 39:22axon guidance functions. So they
• 39:23basically tell everything else where
• 39:25to go. They set the
• 39:26stage for how do we
• 39:28make these circuits functional.
• 39:30And so
• 39:31one of the most well
• 39:33known functions of the subplate
• 39:34is to help thalamocortical
• 39:36projections develop.
• 39:37So how do we bring
• 39:39information from the thalamus to
• 39:40the cortex? The subplate helps
• 39:41sets all of that up.
• 39:43So importantly, we can see
• 39:45in many mouse models that
• 39:46disruption of subplate can impair
• 39:47initial wiring of the lamina
• 39:49cortical circuit. So here
• 39:51in green on the top,
• 39:52again, in brown, you can
• 39:54see all of the whisker
• 39:55barrels of a control mouse.
• 39:57So again, this is sensory
• 39:58information from one whisker of
• 39:59the mouse is each whisker
• 40:01barrel.
• 40:03But in a scenario where
• 40:04we
• 40:05disrupt subplate development,
• 40:07you can see that there
• 40:08is no longer stereotyped and
• 40:09organized sensory input.
• 40:11So
• 40:13while we investigate early cortical
• 40:15connectivity, the subplate is likely
• 40:16crucial to mediating that, and
• 40:18we can kind of piece
• 40:19the two parts together and
• 40:20say, okay. These are the
• 40:21early circuits that are made.
• 40:23What's really directing them to
• 40:25make those connections in the
• 40:26first place?
• 40:27But at the end of
• 40:28the day, it's kind of
• 40:29why do we care? Why
• 40:30is this relevant for us?
• 40:32And so
• 40:33subplate neurons are also altered
• 40:35in cases of neurodevelopmental
• 40:36disorders. So for example,
• 40:39postmortem brains of patients with
• 40:40autism and schizophrenia
• 40:42been reported to have increased
• 40:43subplate remnants. Right? This is
• 40:45a transient population that's supposed
• 40:46to die off really early
• 40:48postnatally.
• 40:50But in cases of autism
• 40:51and schizophrenia, we've seen an
• 40:52increase in the number of
• 40:53neurons that remain.
• 40:56They've also been
• 40:57reported at a different density
• 40:59and different localization.
• 41:01So there's more,
• 41:02they're in places they shouldn't
• 41:04be,
• 41:05and they're too close or
• 41:06too far from each other.
• 41:08So all this to say
• 41:09that by mapping the early
• 41:10developmental connectivity of the cortex,
• 41:12we're able to get
• 41:13both after the cell types
• 41:15and gene expression that orchestrate
• 41:16brain circuits and ultimately how
• 41:18altering these processes
• 41:20can change circuit wiring in
• 41:22different brain disorders.
• 41:23So when we mess something
• 41:24up,
• 41:25when we change something, what
• 41:26does that really do, and
• 41:27should we expect something different
• 41:29when we look at imaging
• 41:30data?
• 41:31So overall,
• 41:32just a general summary is
• 41:34that we know genetic mutations
• 41:36can affect the development of
• 41:37brain circuits, but mice are
• 41:38a really crucial tool for
• 41:40us to be able to
• 41:40assess what's truly going on.
• 41:44Fine resolution tracing of early
• 41:46functional developmental circuits is possible
• 41:49using rabies virus three d
• 41:51brain imaging and single cell
• 41:52RNA seq to
• 41:54kind of integrate what cells
• 41:55these are, where they're connecting
• 41:56to, and do it across
• 41:58time.
• 41:59And then by using these
• 42:00more
• 42:03cross species comparisons, it may
• 42:05reveal conserved features that underlie
• 42:07developmental circuit wiring that's
• 42:09relevant to brain disorders and
• 42:11may also lead us in
• 42:12avenues
• 42:13where something is not present
• 42:14in mice, but may be
• 42:15present in human. And this
• 42:17advancement
• 42:18may kind of be at
• 42:19the heart of of
• 42:24possible disruption.
• 42:26And so with that,
• 42:28both Nanad and Karthik's labs
• 42:30as well as Christian's lab
• 42:31have been integral in setting
• 42:33this up,
• 42:34and I'd be happy to
• 42:35take any questions.
• 42:53I mean, how can you
• 42:54not be impressed by those
• 42:55images of the brain? They're
• 42:56just so, so cool.
• 42:58I was just wondering, maybe
• 42:59I missed is there a
• 43:00parallel sets of experiments going
• 43:02on with, maternal immune activation
• 43:04to look at,
• 43:06connectivity
• 43:07in in the postnatal period
• 43:08as well? So we haven't
• 43:10really considered doing that, but
• 43:11it's a good idea. Right
• 43:13now, our initial focus is
• 43:15to kind of stick with,
• 43:16the genetic mutations and see
• 43:19That's
• 43:19a bit more of
• 43:22a straightforward approach for us.
• 43:23Right? It's more of a
• 43:25binary. And so it's a
• 43:26good question,
• 43:27and maybe something for for
• 43:29future postdocs down the road.
• 43:30Thanks.
• 43:37Oh yeah, so I was
• 43:40asking,
• 43:42so Dan was presenting work
• 43:43on genetic manipulations
• 43:45and how that alters
• 43:46cortical connectivity
• 43:48And, I know in Karthik's
• 43:49lab, they're very interested in
• 43:50maternal immune activation, so infection,
• 43:53and how that can also
• 43:54alter kind of brain wiring.
• 43:56And so I was just
• 43:57wondering if they'd use that
• 43:58rabies tool to look at
• 44:00the connectivity
• 44:01following maternal
• 44:02infection or immune activation
• 44:04to see how that could
• 44:05alter connectivity because there's at
• 44:07least
• 44:08in the offspring in the
• 44:09offspring. Yeah. With this kind
• 44:11of idea that maternal immune
• 44:12activation has been associated with
• 44:14increased risk for psychiatric disorders,
• 44:15including schizophrenia that are associated
• 44:17with ultra connectivity.
• 44:26Impressive. This is completely outside
• 44:28of my wheelhouse. I'm a
• 44:29clinical, you know, scientist for
• 44:30that does therapy work. But
• 44:32I'm wondering if I'm thinking
• 44:33about this as it might
• 44:35apply to,
• 44:37aggressive behavior or impulsivity.
• 44:39And so if if thinking
• 44:41about cortical connections, and do
• 44:42you think there is some
• 44:43relevance to this kind of
• 44:44work to studying
• 44:46that down the road in
• 44:48Yeah. I think so. It
• 44:49impacts humans and their impulsivity.
• 44:51Mhmm.
• 44:52So impulsivity,
• 44:54unsure because I don't know
• 44:55how you study that in
• 44:57mice. I don't have a
• 44:58a behavioral background, so,
• 45:00not my area either. But
• 45:04different types of behavior. So
• 45:05let's say
• 45:06there's altered
• 45:08connections between between the amygdala
• 45:10and the frontal cortex,
• 45:12but we may see difference
• 45:13in anxiety or fear or
• 45:14something else.
• 45:16And, typically, we use,
• 45:19just different retrograde or anterograde
• 45:21tracers.
• 45:22But this may be useful
• 45:24to see where they're actually
• 45:25forming functional synapses as opposed
• 45:28to the typical location.
• 45:30And so seeing, okay. They're
• 45:31not going to prefrontal cortex,
• 45:32but
• 45:33what are they connecting to
• 45:34now that may make a
• 45:35difference? Maybe it's not simply
• 45:37the loss of connections, but
• 45:39the alteration of those connections
• 45:40instead. So I think yes.
• 45:43And
• 45:44Nanad's lab has Nanad's lab
• 45:46has done a little bit
• 45:47of behavior on altered circuits,
• 45:51but we hadn't applied the
• 45:53the rabies virus approach. So
• 45:55we haven't truly identified the
• 45:58individual connections at that level.
• 46:00Thank you.
• 46:03Any other questions?
• 46:05I have two.
• 46:06So the first question is,
• 46:09we study EEG in the
• 46:10lab. We use EEG.
• 46:11And
• 46:12lamina cortical connectivity is known
• 46:14to be a major driver
• 46:15of the EEG that we
• 46:17measure.
• 46:18And you're able to,
• 46:19it sounds like to me,
• 46:20although I'm novice
• 46:22at your level of neuroscience,
• 46:24able to,
• 46:26affect the laminar
• 46:27development of, in the cortex.
• 46:30And so I'm wondering,
• 46:31and I know that people
• 46:32do EEG in mice,
• 46:34little caps.
• 46:36Are they able to,
• 46:38affect those,
• 46:40laminar structures and then be
• 46:43have some sense of how
• 46:44what that does to EEG.
• 46:45Right? Right now, it's a
• 46:46black box for us. So
• 46:47I'm wondering
• 46:48Yeah. What
• 46:49I'm not a hundred percent
• 46:51sure. I imagine yes.
• 46:55So for example
• 46:57oh, the sharing is gone.
• 46:58But,
• 46:59if we disrupt the barrels,
• 47:00let's say, even if we
• 47:02don't affect the lamination, if
• 47:03we just disrupt where those
• 47:04projections are going, we should
• 47:06see a shift in the
• 47:07input
• 47:08activity. So if there's no
• 47:09longer stereotyped whisker barrels, we
• 47:11would expect that in the
• 47:12EEG,
• 47:13you would no longer get
• 47:14kind of those,
• 47:16robust signals where the somatosensory
• 47:18cortex is and maybe more
• 47:21widespread
• 47:21but lower level.
• 47:23So EEG relies on the
• 47:24columnar structure of the of
• 47:26the cortex, and you're disrupting
• 47:27that, and that would affect
• 47:28the EEG. Okay. Yeah.
• 47:30And my second question, this
• 47:31is a colleague of mine.
• 47:35A colleague of mine studies,
• 47:36premature babies.
• 47:38And he's following them into
• 47:40adulthood. And what he's noticing
• 47:41is that they are more
• 47:43avoidant, so bridging the two
• 47:44talks.
• 47:45And so,
• 47:46you know, I know there's
• 47:47some disruption in in terms
• 47:49of lamocortical connectivity
• 47:51with prematurity.
• 47:52And I'm wondering, you know,
• 47:54do people do animal studies
• 47:56on prematurity
• 47:57and looking at avoidance as
• 47:59a as an outcome?
• 48:01A good question.
• 48:02Again, not one hundred percent
• 48:04sure, but I'm
• 48:07prematurity,
• 48:09I would imagine the max
• 48:10is only one day or
• 48:12so in the mouse. So
• 48:13we can we can
• 48:15have the mice delivered one
• 48:16day early, so eighteen days
• 48:18instead of nineteen. But the
• 48:20earlier you go, they won't
• 48:22survive outside the womb.
• 48:24Like I said, the the
• 48:26p zero stage is about
• 48:29late mid fetal.
• 48:30So if we try to
• 48:31deliver earlier than that, it's
• 48:33gonna be really, really difficult.
• 48:40There are ways
• 48:42aside from disrupting the cortex
• 48:44to disrupt thalamocortical
• 48:45innervation, though. If we,
• 48:48mess with the thalamus a
• 48:49little bit, we're able to
• 48:50affect where their axons go
• 48:51as well or their projections
• 48:52go. So that's maybe an
• 48:54alternative
• 48:55to if you see something
• 48:57in
• 48:59imaging data from premature
• 49:02human births,
• 49:04then we may be able
• 49:04to model that back into
• 49:06the mice and say, okay.
• 49:07Now let's direct the circuits
• 49:08to form elsewhere. What really
• 49:10happens with the mice?
• 49:12I see. It's a diffuse
• 49:13insult, really. So it's Mhmm.
• 49:15It's yeah. Yeah. Okay. Well,
• 49:16thank you for your your
• 49:18answer and for this wonderful
• 49:19talk. Any other questions?