# Yale Psychiatry Grand Rounds: October 30, 2020

October 30, 2020## Information

Sarah Yip, PhD, MSc, Assistant Professor of Psychiatry, Yale School of Medicine: "Connectome-based Prediction of Abstinence Across Drugs and Brain States"

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- 00:00Before we get started,
- 00:01I'd like to say a few introductory remarks.
- 00:061st, just a reminder to keep your
- 00:08microphones on mute during the talk
- 00:09and during the discussion less you
- 00:11are speaking during that discussion.
- 00:13Also, if you'd like to ask questions,
- 00:15please hold them to the end of the
- 00:17talk or place them in the chat.
- 00:19In doing so, I'd like to encourage
- 00:21everyone to maintain a respectful stance,
- 00:23nuances, sometimes hard to discern
- 00:25in the format of a zoom call,
- 00:27and so it pays to be particularly
- 00:30thoughtful about this. Next,
- 00:32for those of you who are joining
- 00:34us from other institutions,
- 00:36a special welcome like attendees from Yale.
- 00:38If you'd like to receive CME credit,
- 00:41you may Trisha Doll will post information
- 00:44in the chat about how to sign up.
- 00:47She'll also post information
- 00:48about how to receive credit for
- 00:50participation in today's lecture.
- 00:53Finally, I'd like to announce that
- 00:55the Grand Round speaker next week
- 00:57will be Dean moves from Caltech,
- 00:59who will be speaking on space, time and fear.
- 01:02Survival decisions along defensive circuits,
- 01:04so I encourage you to attend
- 01:06next week as well.
- 01:07So with these introductory remarks behind us,
- 01:09I'm pleased to introduce our
- 01:11Grand round speaker Doctor Sarah.
- 01:12Yep.
- 01:13Doctor Yip received her PhD in
- 01:16psychiatry from the University of Oxford
- 01:19and in the United Kingdom in 2013.
- 01:23Subsequently,
- 01:23she came to Yale to complete a
- 01:25two year fellowship in Addiction
- 01:27Psychiatry within the Division of
- 01:29Substance Abuse in our Department.
- 01:31She was then promoted to Associate
- 01:33research Scientist and subsequently
- 01:35to assistant professor in 2016,
- 01:37and she holds a joint appointment
- 01:39in the Child Study Center.
- 01:41Ann in psychiatry.
- 01:43While at Yale,
- 01:44she is used Nuro psychiatric
- 01:46research methods to identify the
- 01:48biological mechanisms of addictions
- 01:50and their treatments.
- 01:51To support her efforts,
- 01:53she's been remarkably successful
- 01:54in obtaining external funding,
- 01:56including from the UK's Medical Research
- 01:59Council, Beijing Normal University,
- 02:00the National Center for Addiction,
- 02:02Substance Abuse,
- 02:03the Brain and Behavior Research Foundation,
- 02:05Naida, and, most recently,
- 02:07in IEEE.
- 02:09In this work,
- 02:10she's applied machine learning
- 02:12approaches to identify predictive
- 02:14neural mark rows of cocaine and
- 02:16opioid use as well as collection of
- 02:18neuroimaging data from individuals
- 02:20receiving different forms of medication
- 02:22assisted treatments for opioid
- 02:24use disorder or pharmacological
- 02:25challenges with medications,
- 02:27and most recently she's received
- 02:28funding from an I AAA to include
- 02:31predictive modeling of neuroimaging
- 02:33data from three large developmental
- 02:35cohorts to identify neural
- 02:36markers of alcohol initiation
- 02:38and misuse. Promises to be a
- 02:41very important work.
- 02:42Of note, in 2019 her work on
- 02:45connectome based modeling to predict
- 02:48treatment response in cocaine use
- 02:50disorder was cited as a top as the
- 02:54top basic science achievement of 2019
- 02:56by night as director Norvo Cough.
- 02:59Today we'll have the opportunity
- 03:01to learn more about this important
- 03:03work in her talk entitled Connectome
- 03:06based prediction of absence across
- 03:08drugs and brain states welcome Sarah.
- 03:12Thank you Stephanie,
- 03:13for that great introduction.
- 03:14It was one of the top basic
- 03:16science achievements, not the talk.
- 03:20I'm happy to provide support.
- 03:22That's our perspective.
- 03:23OK, let me just get my screen sharing.
- 03:33Can anyone see my slides?
- 03:37Yep yeah great OK great.
- 03:38So yes, Stephanie said I'm going to
- 03:41be presenting today on connectome
- 03:43based prediction of abstinence
- 03:44across drugs in Britain states.
- 03:46So this is work that my lab has
- 03:48been doing for a number of years
- 03:49to try to identify brain based
- 03:51predictors of treatment outcomes.
- 03:54I'm sure everyone here is aware of the
- 03:57current substances epidemic in this country,
- 03:58but I think it's nonetheless important to
- 04:00mention so this color map shows population
- 04:02level deaths per 100,000 individuals in the
- 04:05United States back in 2014, and as we know,
- 04:07these rates have continued to rise.
- 04:09And so, for example,
- 04:11annual opiate associated fatalities
- 04:12have exceeded those calls by firearms
- 04:14and motor vehicles combined,
- 04:15as well as those caused by HIV at
- 04:17the height of the AIDS epidemic.
- 04:19And although it's been much less publicized,
- 04:22there has been this concurrent rise and
- 04:23cocaine and stimulant associated fatalities.
- 04:25Therefore, we really do need improve
- 04:27strategies to combat the current
- 04:29substance use epidemic in this country,
- 04:31which is really the motivation
- 04:33for my labs or imaging work.
- 04:35So I often get asked why we would
- 04:37even using our imaging to predict
- 04:39treatment outcomes in the 1st place.
- 04:41After all,
- 04:42we're pretty fortunate in the addiction
- 04:43field with a large number of really
- 04:45excellent evidence based treatments that
- 04:47are very effective for some individuals.
- 04:49However,
- 04:49we also have a lot of between
- 04:51patient heterogeneity,
- 04:52which unfortunately means that the overall
- 04:53efficacy of any given treatment tends to
- 04:55be highly variable across individuals.
- 04:57Given this heterogeneity,
- 04:58it's perhaps not surprising that
- 04:59even gold standard treatments and
- 05:01have a high rates of relapse and that
- 05:03most individuals with a substance
- 05:04use disorder go through multiple
- 05:06failed treatment attempts.
- 05:07In other words,
- 05:08when it comes to addiction treatment,
- 05:10one treatment really doesn't
- 05:12fit all relapse rates.
- 05:13Following treatment also remain high,
- 05:15and for some substances this
- 05:16is a critical vulnerability.
- 05:17Vulnerability period for
- 05:19overdose associated death.
- 05:20And this problem is really further
- 05:22exacerbated by the fact that what I like
- 05:24to call traditional clinical variables.
- 05:26So for example,
- 05:27things like baseline severity don't tend
- 05:28to be all that helpful for predicting
- 05:30treatment response for relapse rates,
- 05:32meaning that we have unexplained
- 05:33sources of her originality
- 05:35influencing treatment outcomes.
- 05:36So within this context,
- 05:37a number of research groups have
- 05:38turned in our imaging measures
- 05:40to try to identify brain based
- 05:41predictors of treatment response.
- 05:45And so over the past 10 to 15 years,
- 05:48there's been a number of proof of
- 05:51concept studies looking brain,
- 05:52function and structure to
- 05:53treatment outcomes and addictions.
- 05:55So, for example,
- 05:56work by our group and others
- 05:57indicate that individual differences
- 05:59in pretreatment reward related
- 06:01activations are prospectively linked
- 06:03to within treatment abstinence.
- 06:05That changes in reward related activations,
- 06:07Ann May Co occur with reductions
- 06:09in substances and that individual
- 06:11differences in functional connectivity
- 06:12relate to both current cocaine use,
- 06:15an feature relapse to cocaine.
- 06:18So collectively,
- 06:18these and other studies are really
- 06:20supporting this overarching hypothesis,
- 06:21that individual differences in some
- 06:23aspects of the brain are indeed linked
- 06:26to differences in treatment outcomes.
- 06:28However,
- 06:28this work also has some limitations,
- 06:30so in particular most prior studies
- 06:32have relied on prospective associations
- 06:34and have used methods such as
- 06:36correlation and regression that
- 06:37have a tendency to overfit the data,
- 06:39leading to inflated effect size estimates.
- 06:41And so the problem with using the term
- 06:43prediction in this context in the
- 06:45context of symbol correlation or regression,
- 06:47is that true prediction requires
- 06:49application of a model to novel data.
- 06:52Machine learning approaches seek to
- 06:54produce overfitting via creation of
- 06:56a predictive model in a data driven
- 06:58manner manner using the training data set.
- 07:00And then application of that model
- 07:02to an independent test data set which
- 07:04is used for model validation and
- 07:05that distinction is really important
- 07:07because whereas the aim of traditional
- 07:09statistical approaches is to explain
- 07:11the relationship between two variables,
- 07:13the aim of machine learning approaches is
- 07:15to generate predictions in novel data,
- 07:17and I would argue that this type of
- 07:19approach is critical for the eventual
- 07:21translation of research findings
- 07:22into clinical settings,
- 07:24which is really a primary
- 07:25challenge of modern psychiatry.
- 07:27In addition, if used correctly,
- 07:28these approaches can be used
- 07:30for normal biological discovery.
- 07:31Which to me is also really important.
- 07:34So in my opinion,
- 07:35in addition to generating predictions
- 07:37in novel data,
- 07:38a primary goal of brain based
- 07:40clinical modeling should really be
- 07:42elucidation of mechanism.
- 07:43However, as we know,
- 07:44even highly predictive models
- 07:46often can be very little to enhance
- 07:49our mechanistic understanding.
- 07:50So throughout this talk,
- 07:51I'm really going to focus on maximizing
- 07:54nor biological discovery within the
- 07:56context of predictive modeling.
- 07:57In order to maximize this type of discovery,
- 08:00we use data driven whole brain methods.
- 08:02The reason for this is that given
- 08:03that recovery from addiction involves
- 08:05complex interactions across across
- 08:06clinical and biological domains,
- 08:08we think it's likely that it's even
- 08:09of abstinence also involves distributed
- 08:11processes for multiple brain regions.
- 08:13Therefore,
- 08:13all of the data and we showed you
- 08:15today is using a whole brain
- 08:17connectivity based approach,
- 08:18in which we're focusing on patterns
- 08:20of functional activity across the
- 08:22entire brain.
- 08:23The specific method I'm doing focusing
- 08:24on throughout this talk is referred
- 08:26to as connectome based predicted modeling,
- 08:28and unlike some other machine
- 08:30learning approaches,
- 08:30this is an entirely data driven technique.
- 08:32It doesn't require any a priori
- 08:34specification of networks or
- 08:35regions of interest.
- 08:36Therefore it's not only a predictive tool,
- 08:38but it's also a method
- 08:41of identifying networks.
- 08:42And so, prior to the data will be presenting.
- 08:45Today, connectome based modeling had
- 08:46been used to generate robust predictive
- 08:48models of measures such as IQ and attention,
- 08:51but it had never been applied to predict
- 08:54future behavior or clinical outcome.
- 08:57When we use a connectivity based approach,
- 08:58what we're doing is we're extracting time
- 09:00courses activity from multiple regions.
- 09:02It together encompass the entire
- 09:03brain to represented here by these
- 09:05cards in green and red voxels,
- 09:06and we simply correlate the patterns
- 09:08of activity across the time course of
- 09:10the whole scan to get a single summary
- 09:11statistic that summarizes the temporal
- 09:13coherence between those two brain regions.
- 09:15And we can do this for every single
- 09:17voxel in the brain to create a
- 09:19functional connectivity matrix or
- 09:21what we refer to as a connectome.
- 09:22So this single matrix Now summarizes
- 09:24whole brain connectivity patterns
- 09:26for all of the voxels in the brain.
- 09:27Or a single person,
- 09:28and there's data to indicate that
- 09:30these connectomes are both relatively
- 09:32unique to the individual,
- 09:33but also the patterns of connectivity
- 09:35within the connectome.
- 09:36Forgiven individual may vary as a
- 09:39function of task performance or what
- 09:41we like to refer to his brain state.
- 09:44The consistent with this notion of
- 09:45brain stay work by colleagues here
- 09:47at Yale has demonstrated that the
- 09:49accuracy of predictive models generated
- 09:50from connectivity data is improved
- 09:52when the input is connectivity data,
- 09:54computed joint task performance
- 09:56as opposed to join resting state.
- 09:57So here on your left you have the percent.
- 10:00Variance in IQ is explained by a
- 10:02predictive model built using different
- 10:03tasks from the Human Connectome Project.
- 10:05An on your right you have the
- 10:06same thing for the Philadelphia
- 10:08in or developmental Gore,
- 10:09and what I want to point out here
- 10:11is it in both cohorts the predictive
- 10:13accuracy of models built using
- 10:14resting state data is relatively low,
- 10:16so in both cases the model is only able to
- 10:19account for about 4% of the variance in IQ.
- 10:21The second thing I want to highlight
- 10:23is that the predictive accuracy of
- 10:25models generated from different types
- 10:26of task data is also highly variable,
- 10:28with some tasks accounting for
- 10:29up to 12% of the variance in IQ.
- 10:32Another accounting for less,
- 10:33so together this is suggesting that
- 10:34specific brain statement elations mean
- 10:36preferable for predicting a given behavior,
- 10:38which is an issue that I'm going to
- 10:40return to you throughout this talk,
- 10:42which brings me to the first bit of
- 10:44data will be presenting today in which
- 10:46we're using a connected based approach
- 10:48to try to put it to the clinical outcome.
- 10:50In this case,
- 10:51abstinence from cocaine during
- 10:52a 12 week treatment.
- 10:54The study design for this
- 10:55is relatively simple.
- 10:56We recruited patients and during
- 10:57a 12 treatment trial and scan them
- 10:59at the start of treatment and
- 11:00then again following treatment.
- 11:02All of our participants receiving
- 11:03methadone maintenance therapy for opiate
- 11:05use disorder, but we're now entering
- 11:07treatment for cocaine use disorder,
- 11:08so these are polysubstance using individuals.
- 11:11I'm not going to talk about the specific
- 11:13aspects of the 12 week trial today,
- 11:16but if you're interested,
- 11:17this was an RCT of behavioral therapy,
- 11:19with or without treatment with a
- 11:21cholinesterase inhibitor lanta mean,
- 11:22so the results of that are
- 11:24published in the Journal.
- 11:25Kinda looks like hydrate and
- 11:27consistent with the overall arc TR,
- 11:29nor imaging subsample was dominantly male,
- 11:30there mostly unemployed and the primary route
- 11:33of cocaine administration was via smoking.
- 11:35They also did a number of
- 11:37prior treatment attempts,
- 11:38including an average of three or more
- 11:39prior failed inpatient treatment attempts,
- 11:41and three or more for three or more prior
- 11:43failed outpatient treatment attempts,
- 11:45and they also had significant legal problems,
- 11:47so this is a very treatment
- 11:49refractory population, but it's also,
- 11:50you know, kind of a.
- 11:53It's not an unusual population.
- 11:56I'm not really going to drill down too
- 11:58much into the methods of CPM today,
- 12:00but I'm happy to answer questions
- 12:02if people are interested,
- 12:03but I do just want to give you sort
- 12:05of a general overview of the approach.
- 12:07So practically when we're doing
- 12:08a connecting based model,
- 12:10what we're doing is we're taking
- 12:11individual connectomes from a training
- 12:13data set and relating them to a
- 12:14behavioral variable of interest.
- 12:15So in this case that within treatment,
- 12:17abstinence and we do this using simple
- 12:19correlation in order to identify
- 12:20positive productive connections,
- 12:21or what we refer to as edges,
- 12:23as indicated by these red squares
- 12:24here and then we also identifying
- 12:26negative prediction.
- 12:27Productive connections as indicated
- 12:29by the green squares.
- 12:30So positive predictive connections
- 12:32are connections for which increased
- 12:34connectivity predicts absence,
- 12:35whereas negative predictive connections
- 12:37or connections for which decreased
- 12:39connectivity predicts abstinence.
- 12:40So this is our feature selection phase.
- 12:43We then create individual participant
- 12:44summary scores via just summing
- 12:46the edge weights identified in our
- 12:48feature selection phase so that each
- 12:49participant now just has two values.
- 12:51One is that positive summaries for
- 12:52another is a negative summers,
- 12:54or we then create our brain behavior
- 12:55model by entering these summary
- 12:57scores into predictive models.
- 12:58In this case, assuming linear relationships,
- 13:00so a simple Y equals MX plus B and
- 13:02then finally we apply this model to our
- 13:04testing data so we take connectivity
- 13:06matrices from the independent data set.
- 13:08We extract connectivity values from the
- 13:10edges we identified in our training data,
- 13:11set some them to create.
- 13:13Summary scores for new participants
- 13:15and enter those values into our brain
- 13:17behavior model to generate individual
- 13:19predictions of within treatment abstinence.
- 13:21And finally,
- 13:21although it's not shown here,
- 13:23we evaluate the performance of our
- 13:25model by comparing those predicted
- 13:26abstinence values with the actual
- 13:28within treatment abstinence values.
- 13:30And so we can compare actual
- 13:31predicted values.
- 13:32A number of different ways,
- 13:33which is an issue.
- 13:34I'm not really going to go into in this talk,
- 13:36but really the simplest way
- 13:38is just brain correlation.
- 13:39So that's what I'm going to be
- 13:41using to assess model performance
- 13:42throughout the talk today.
- 13:44For this study, with a couple of different
- 13:46types of functional data to choose from.
- 13:48So during pretreatment scanning,
- 13:49everyone perform two tasks.
- 13:50One was a basic cognitive control task
- 13:52and the other one was a classic reward
- 13:55task and monetary incentive void task.
- 13:57And so, given that we were
- 13:59interested in cocaine use.
- 14:00We chose to either word
- 14:02task because at that time,
- 14:03at least to my mind,
- 14:05the data linking cocaine use to
- 14:06reward was stronger than the data
- 14:08linking it to cognitive control,
- 14:10and so using the data acquired
- 14:11Dreamer Award task,
- 14:12we compute individual participant
- 14:14connectivity matrices and we feed this
- 14:15into our connectome based model along
- 14:17with our measure of instrument abstinence,
- 14:19which was defined as the percentage
- 14:21of BI weekly urine specimens
- 14:22that were negative for cocaine
- 14:24during the 12 week treatment.
- 14:26So is the biologically verified
- 14:29dimensional measure of absence?
- 14:31So here we have our initial model
- 14:33results here on the Y axis we have
- 14:35individual participant abstinence values
- 14:37as predicted by our brain behavior
- 14:38model and on the X axis we have actual
- 14:41absolute values for each participant.
- 14:43And so typically when we use correlation,
- 14:45we're trying to explain the
- 14:46variance between two variables.
- 14:47But here we're just using it to
- 14:50characterize predictive accuracy
- 14:51or the correspondence between
- 14:52actual and predicted values.
- 14:54And so you can see that our model
- 14:56has relatively good predictive
- 14:57accuracy with this German 0.42,
- 14:59which means that about 20% of the
- 15:01variance in within treatment abstinence
- 15:02is accounted for by connectivity
- 15:04within our abstinence networks,
- 15:05and interesting Lee,
- 15:06this correspondence is greater
- 15:08than that absorbed.
- 15:09If we just relate absence
- 15:11to another level variable,
- 15:12for example related to baseline cocaine use,
- 15:14indicating that are connected based
- 15:16model has greater predictive accuracy
- 15:18than traditional clinical variables.
- 15:19So in addition to generating predictions,
- 15:21this approach also,
- 15:22of course,
- 15:23identifies networks just as a heads up.
- 15:26Networks identified using whole
- 15:27connectome based approaches are
- 15:29typically complex and can be
- 15:30composed of multiple adjacent
- 15:32and non Jason's brain regions.
- 15:33As was certainly the case here.
- 15:35So here on your left you can see the
- 15:38positive network shown in red as a reminder.
- 15:41The positive network includes connections
- 15:43which increased connectivity predicts
- 15:45absence and on the right you can see
- 15:47the negative network which corresponds
- 15:49to connections which decreased
- 15:51connectivity predicts abstinence.
- 15:52And so while these networks are
- 15:54certainly complex and arguably
- 15:55pretty hard to interpret when
- 15:56presented in this fashion,
- 15:58it's important to point out that actually
- 16:00the combined number of connections
- 16:01within the positive and the negative
- 16:03network together is only around 500,
- 16:05which is actually less than than
- 16:062% of all possible connections.
- 16:09In other words,
- 16:10despite this visual complexity,
- 16:12these are actually quite
- 16:13specific connections.
- 16:17So there are a number of standard
- 16:19ways in which we can now start
- 16:21to summarize these connections
- 16:22to facilitate interpretation.
- 16:23So one simple way of characterizing
- 16:25our networks is to summarize
- 16:27them by a connection distance.
- 16:28So for each nodal connection we can
- 16:30compute Euclidean distance using
- 16:32central coordinates for each node.
- 16:33And if we apply this to
- 16:35our abstinence networks,
- 16:36what we find is that both networks include
- 16:38both short and long range connections.
- 16:40However, positive absence network,
- 16:41the network for which increased connectivity
- 16:43predicts more within treatments,
- 16:44is characterized by predominantly
- 16:46longer interactions.
- 16:47Where is our negative network,
- 16:48the network for which decreased connectivity
- 16:50predicts more within treatment.
- 16:52Abstinence is characterized by
- 16:53predominantly shorter range connections,
- 16:54which makes intuitive sense
- 16:56because longer range connections
- 16:57tend to be involved in the higher
- 16:59order more complex processes.
- 17:03Another way in which we can summarize
- 17:04these networks is of course,
- 17:06anatomically so these circle plots
- 17:07summarize network connectivity based on
- 17:09the number of connections between mappers.
- 17:10They'll brain regions which
- 17:11are listed on your left,
- 17:13so from the top of each circle brain brain
- 17:15regions are represented in grafana top,
- 17:17and as amical order,
- 17:18with lines coming from the
- 17:19red portions of the top,
- 17:21or responding to prefrontal cortical
- 17:22connections in lines coming from
- 17:24the purple bits at the bottom
- 17:26corresponding to brainstem connections.
- 17:27And from left to right,
- 17:28network connectivity is threshold
- 17:29with a different levels based on the
- 17:31total number of connections for brain,
- 17:33region and so you can see that if we
- 17:35use a liberal threshold, for example,
- 17:37we look at all brain regions with five
- 17:39or more connections showing here on the left.
- 17:41Our networks remains somewhat
- 17:42difficult to interpret.
- 17:43However,
- 17:43if we take a more conservative threshold,
- 17:45for example only focusing on regions with
- 17:4712 or more connections on the right.
- 17:49Our networks begin to be a bit
- 17:51more interpretable,
- 17:52so for example,
- 17:53we can see that the positive abstinence
- 17:55network shown in red at the top is
- 17:57characterized by right prefrontal node
- 17:58with connections to temporal limbic
- 18:00and left frontal cortical regions,
- 18:01and we can also see that the negative
- 18:04network here at the bottom is
- 18:06characterized by a temporal node with
- 18:08connections to limit and cerebellar nodes.
- 18:10Another way we can begin to understand
- 18:12the anatomy of our absence networks
- 18:14is via their spatial overlap
- 18:15with Canonical neural networks.
- 18:16So for example,
- 18:17via their overlap with the frontal
- 18:19parietal salients and default mode networks,
- 18:21all of which had previously
- 18:23been implicated in addictions.
- 18:25When we do this,
- 18:26we can characterize connectivity
- 18:27based on the number of connections
- 18:29between these established networks.
- 18:31So here we have matrices summarizing
- 18:32overlap between these networks for the
- 18:34positive and negative network separately,
- 18:36and for each matrix the cells represent
- 18:38the total number of connections
- 18:39within and between each network,
- 18:41with darker colors indicating a
- 18:42greater number of connections,
- 18:44and so we can see the positive
- 18:46network is characterized by a large
- 18:47number of frontal parietal to me
- 18:49and medial frontal connections,
- 18:50as well as by a lot of salience
- 18:53and motor sensory connections as
- 18:55indicated by these dark red boxes.
- 18:57We can also see that the negative network
- 18:59includes a large number of salience,
- 19:01default mode, and medial frontal connections,
- 19:03as indicated by the dark blue boxes,
- 19:05and these differences become even
- 19:06more apparent if we directly compare
- 19:08the number of connections within
- 19:10each network so we can see that the
- 19:12positive network includes more frontal
- 19:13parietal to medial frontal connections,
- 19:15as well as more salient subcortical
- 19:16motor sensory connections.
- 19:17In contrast,
- 19:18the negative network includes more
- 19:20connections between the medial
- 19:22frontal network and the salience
- 19:24in default mode connections.
- 19:25So based on these differences,
- 19:27we generated a theoretical network
- 19:28based model of cocaine abstinence.
- 19:30We propose the cocaine abstinence
- 19:32is positively predicted by increased
- 19:33connectivity within and between frontal
- 19:35parietal and medial frontal networks.
- 19:37As a reminder, these are networks
- 19:39involved in coordination of attention
- 19:40and executive control processes,
- 19:42and so we think they might contribute
- 19:44within two and abstinence via
- 19:45coordination of the top down process
- 19:47is necessary for treatment engagement.
- 19:50So for example,
- 19:50things like acquisition of new
- 19:52skills or enhanced control over
- 19:54impulsive behaviors or data further
- 19:55suggests that cocaine abstinence.
- 19:57Is positively predicted by
- 19:59increased connectivity.
- 20:00Within salient subcortical motor sensory
- 20:02regions and so these are networks
- 20:04involved in coordination of salience,
- 20:06encoding and reward.
- 20:07So we think that these networks
- 20:09could support motivational
- 20:10processes and relevant in treatment.
- 20:12For example,
- 20:12willingness to change in shoring up
- 20:14of non drug reward processing or
- 20:17attending double turn it rewards.
- 20:18Finally,
- 20:19our data indicate that absence
- 20:20is further predicted by decreased
- 20:22connectivity between these two systems
- 20:24and so based on prior connectivity
- 20:26working cocaine use disorder,
- 20:28we actually think that appropriate
- 20:29separation between these two systems.
- 20:31Could relate to greater behavioral
- 20:33flexibility or decreased compulsivity
- 20:34as might be required for behavior
- 20:36change during treatment,
- 20:37so this model is really building
- 20:38on prior models of addiction
- 20:40emphasizing separation of frontal,
- 20:41parietal and salience networks
- 20:42such as model put forth by Elliot
- 20:44Stein colleagues where we're also
- 20:46incorporating medial frontal motor,
- 20:47sensory and subcortical networks.
- 20:49Provide a theoretical framework
- 20:50for future research,
- 20:51and so our hope is that by
- 20:53proposing this framework,
- 20:54we can encourage others to test
- 20:56these hypothesis in their own data.
- 20:59So the last thing I want to point out
- 21:01on this slide is that so when we're
- 21:03when we're computing our connectivity,
- 21:05matrices were basically we're
- 21:07taking the time course across the
- 21:08entire rewards get broad tasks,
- 21:10so we're not modeling events
- 21:11of interest at all.
- 21:13We're just basing it on the
- 21:14overall pattern of connectivity
- 21:16across the entire task.
- 21:17But Despite that,
- 21:18I think that our findings are
- 21:19actually somewhat intuitive when we
- 21:21consider them within the specific
- 21:23context of reward task performance,
- 21:24which of course would require
- 21:26coordination of both attentional
- 21:27and cognitive control processes
- 21:28as well as assailants encoding
- 21:30and reward response behaviors.
- 21:31So, so again,
- 21:32this is just starting to hint at
- 21:34this possibility that Brain State
- 21:36might matter for predictive modeling.
- 21:38Also tested the ability of the
- 21:39identified networks for abstinence
- 21:40following treatment.
- 21:41So for this analysis we simply create
- 21:43summary scores by summing edge
- 21:45weights for the nodes identified
- 21:46in our original analysis.
- 21:48So we're taking our absence networks
- 21:49and we're using them as a mask to
- 21:52extract values from post treatment data,
- 21:54and we enter those into correlational
- 21:55analysis with percent days of absence.
- 21:57During six month follow up and we find
- 21:59that post treatment network strengths
- 22:00were in fact significantly correlated
- 22:02with posttreatment abstinence,
- 22:04suggesting relative consistency
- 22:05of this relationship overtime
- 22:06further consistent with that.
- 22:07When we just compare pre
- 22:09versus post treatment.
- 22:10Connectivity strength we see no changes,
- 22:12overtime raising the possibility
- 22:13that these effects are somewhat
- 22:16stable within individuals.
- 22:17But the big question is,
- 22:18is not really whether or not these effects
- 22:20are stable within individuals overtime,
- 22:22but whether or not this fact
- 22:23replicates an independent sample,
- 22:25because that's really the whole
- 22:26point of using something like
- 22:28a machine learning approach.
- 22:30So the analysis of and show
- 22:31you up to now were run using.
- 22:33They gotta leave one out cross
- 22:35validation scheme in which for each
- 22:37for a single participants predicted
- 22:38value or what we refer to as the
- 22:40left out participant is generated by
- 22:42taking the data from all of their
- 22:44participants in the training data
- 22:45set an iterative manner and so all
- 22:47participants have a predicted value.
- 22:49But that approach can also be
- 22:50prone to overfitting,
- 22:51so we wanted to also have an external
- 22:54validation sample which was no small
- 22:56feat because pretreatment Norman
- 22:57data is pretty hard to come by.
- 22:59However, things to Kathy, Carolyn,
- 23:01Mark, but Enza,
- 23:01we managed to cobble together
- 23:03a replication sample.
- 23:04I'm not going to go too much
- 23:05into the details of that today,
- 23:07but it was 45 individuals about
- 23:09a third of whom were taking
- 23:10methadone for opiate use or now
- 23:12seeking treatment for cocaine.
- 23:13As in our original sample,
- 23:14but 2/3 of the replication sample were
- 23:16not on methadone and work independently,
- 23:18and they're also scan prior to
- 23:20us totally separate treatment.
- 23:21Trial involving a different medication.
- 23:22So this is a very heterogeneous
- 23:24replication sample, was at least.
- 23:25For replication sample were again just
- 23:27creating summary scores by summing
- 23:29edge weights with nodes identified in
- 23:31our original analysis and entering
- 23:33them into correlation analysis.
- 23:34Their biological measure absence
- 23:35percent cocaine for yarns and in fact
- 23:38we find the same relationship between
- 23:40network strengthen within treatment.
- 23:41Abstinence in this heterogeneous
- 23:43independent sample.
- 23:44Just to summarize that first data set.
- 23:46These data demonstrate the ability of
- 23:48a recently developed machine learning
- 23:49approach for Dick Truman Outcomes.
- 23:50In this case,
- 23:51abstinence from cocaine germ
- 23:52phobia treatment.
- 23:53I didn't show you the data today,
- 23:55but we recently replicated this
- 23:56in a second external sample.
- 23:58Networks that we are identified with her
- 24:00bus and they were relatively unchanged,
- 24:02involved analysis,
- 24:02controlling for other
- 24:03baseline clinical variables.
- 24:04So for example,
- 24:05methadone dose or days of
- 24:07past month calcaneus.
- 24:08Post treatment connectivity within
- 24:10these networks also predicted
- 24:11absent certain follow up and our
- 24:12networks are not changed from
- 24:13pre to posttreatment raising the
- 24:14possibility that this relationship
- 24:15might be somewhat consistent overtime,
- 24:17which is a point that will come
- 24:19back to at the end of my talk.
- 24:21But first I want to return
- 24:23this issue of Brain State.
- 24:24And which brings me to the
- 24:26second part of my talk,
- 24:27in which we've begun looking into
- 24:29whether brain states are drug specific.
- 24:30So this is work that was recently
- 24:32published and look at their psychiatry and
- 24:34was led by Doctor Sarah Legiance Teen,
- 24:36a very talented former postdoc in
- 24:38my lab who was recently who recently
- 24:40received her appointment to assistant
- 24:42professor here in Psychiatry.
- 24:43So the clinical rationale for
- 24:45this work really came from
- 24:46some MA work done by Epstein
- 24:48and colleagues a few years ago,
- 24:50so this was an electronic
- 24:51diary study of 114 methadone
- 24:52treat individuals with cocaine,
- 24:54an opiate dependence so very
- 24:55similar to our Poly substance.
- 24:57Using our image example,
- 24:58and in this study what they did
- 25:00was ask participants to track
- 25:01changes in mood and craving,
- 25:03and to document their substance use
- 25:05within an animated framework and
- 25:06what they found was a differential
- 25:08Association between positive versus
- 25:09negative mood States and future
- 25:10substances such that cocaine use
- 25:12was most robustly associated
- 25:14with having been exposed to the
- 25:16drug or being in a positive mood.
- 25:18Suggesting possible links
- 25:20with impulsivity and reward,
- 25:21but heroin craving was associated
- 25:23with increases in negative affect,
- 25:25suggesting possible links with emotion
- 25:28regulation or inhibitory control.
- 25:30And so, based on this finding,
- 25:32the different mood states predict opiate
- 25:33versus cocaine use in the same individuals.
- 25:36We wanted to test the hypothesis
- 25:37that maybe different brain states
- 25:39might be more closely linked
- 25:40to opiate versus cocaine use.
- 25:42In our image example.
- 25:44So for this analysis,
- 25:46instead of focusing on cocaine use,
- 25:47we're focusing on opiate use.
- 25:49However,
- 25:49the primary simple is the same,
- 25:51so as I mentioned in the beginning
- 25:53of this talk,
- 25:54when I introduced our sample despite
- 25:55seeking treatment for cocaine use,
- 25:57all of our participants at Coco
- 25:59get use disorder.
- 26:00And we're currently methadone
- 26:01maintained and in fact it did turn
- 26:03out that there was a fair amount of
- 26:05residual to be used in this sample.
- 26:06If you're interested in the details on that,
- 26:08I'll refer you to the clinical
- 26:10paper led by Kathy Carroll,
- 26:11but just briefly so you can sort
- 26:13of understand that data.
- 26:14Here we have survival curves indicating
- 26:15the time to 1st submission of a
- 26:17non methadone will be a positive
- 26:18urine screen during this whole week
- 26:20treatment and we can see that on
- 26:22average about 50% of participants
- 26:23in this study submitted at least
- 26:24one non methadone opiate positive
- 26:26urine during treatment.
- 26:27This is also true of the newer
- 26:29imaging subsample,
- 26:30for which the mean percentage
- 26:32of opiate negative yarns meted
- 26:33during treatment was about 65%,
- 26:35indicating of course at about
- 26:3735% of specimens tested positive
- 26:38for non methadone opiates.
- 26:43For this analysis we again just calculate
- 26:45functional connectivity matrices,
- 26:45but this time we're doing using the
- 26:47data acquired during performance
- 26:48or cognitive control task and we
- 26:50entered those matrices into our
- 26:52print behavior model along with their
- 26:54biological measure of abstinence.
- 26:56And so this is the result of that model
- 26:58here on the Y access we again have
- 27:01individual participant absence values
- 27:02as predicted by the brain behavior model
- 27:04and on the X axis we have the actual
- 27:06abstinence values for each participant,
- 27:08again as a reminder.
- 27:09Typically when we use correlation,
- 27:10we're using it to try to explain
- 27:12the variance between two variables,
- 27:14But here we're just using the up to
- 27:15characterize predictive accuracy
- 27:16or the correspondence between
- 27:18actual encrypted values.
- 27:18And as you can see,
- 27:20our model has relatively good
- 27:21predictive accuracy with aspirin 0.34,
- 27:23which means at about 12% of the
- 27:25variance and within treatment
- 27:26opiate use is accounted for by
- 27:27connectivity within this network.
- 27:29So for Contacts that's comparable
- 27:30to the amount of variance explained
- 27:32by similar approaches that seek to
- 27:34predict rate like behaviors such as IQ.
- 27:36As their cocaine network that
- 27:38will be at network,
- 27:39we identified his complex that includes
- 27:41connections between multiple adjacent
- 27:42and non adjacent brain regions and in
- 27:44fact or opiate network is actually
- 27:45somewhat larger than the cocaine network.
- 27:47So with the positive and
- 27:48negative networks together,
- 27:49including just under 1000 edges.
- 27:52So that's almost twice the size
- 27:54of the cocaine network.
- 27:55However, it's still less than
- 27:573% of possible connections,
- 27:58so again, and despite the visual.
- 28:01Complexity to actually quite
- 28:02specific connections,
- 28:03you can understand the anatomy
- 28:04of her opiate network.
- 28:05We can again summarize it by
- 28:07overlap with Canonical networks.
- 28:08So for example by overlap of medial,
- 28:10frontal and default mode networks
- 28:11when we do this,
- 28:12we can see that at least relative
- 28:14to the cocaine network,
- 28:15the opiate network is somewhat more sparse,
- 28:17and it does not include any within network,
- 28:20medial, frontal,
- 28:20or default mode connections.
- 28:22So despite including more edges overall,
- 28:24that will be it.
- 28:25Network is distributed across fewer
- 28:28Canonical networks.
- 28:29Directly compare positive versus
- 28:30negative networks.
- 28:31We find that positive network we
- 28:32follow the positive network included
- 28:34relatively more within network
- 28:35connections in the motor sensory network,
- 28:37whereas the negative network was
- 28:39characterized by more connections
- 28:41between the Motors and sorry
- 28:42network and rental prior to default
- 28:44mode and medial frontal networks.
- 28:46I have to say we were somewhat
- 28:47surprised that a large number of
- 28:49Motors motor sensor connections here.
- 28:51So what we've done is it also
- 28:52taken a virtual lesion approach
- 28:54with these data in which we just
- 28:56knock out all the nodes overlap
- 28:58with the given conical network,
- 28:59and we find that when we do this,
- 29:01despite the ceilings in child's
- 29:03motor sensory component,
- 29:03if we completely remove it,
- 29:05remaining connections still are
- 29:06sufficient in product abstinence.
- 29:07And actually we find the same
- 29:08thing if we if we knockout other
- 29:10individual clinical networks.
- 29:12For example,
- 29:12if we just knock out the default mode
- 29:14connections, really indicating that those.
- 29:16Single Canonical network alone is
- 29:18required to support abstinence.
- 29:20Salty understanding we again proposed
- 29:22a theoretical model that summarizes
- 29:24key aspects of this network,
- 29:25so this figure just emphasizes
- 29:27the absence was associated with
- 29:28increased within network motor sensor
- 29:30connectivity and increased between
- 29:32network connectivity of motor sensory
- 29:34and salience networks and of default
- 29:36mode and frontal parietal networks
- 29:38as indicated by the red lines here.
- 29:40And models also highlighting the absence,
- 29:42was further associated with decreased
- 29:44connectivity between the motor sensory
- 29:45network and medial frontal default
- 29:47mode and frontal parietal networks,
- 29:48as indicated by the blue lines,
- 29:50and again,
- 29:51our hope is that by summarizing the data
- 29:53in this somewhat simplistic manner,
- 29:55we can encourage others to test these
- 29:57theories in their own datasets to
- 29:59further guide nor biological risk.
- 30:01We've also made all of the masks of
- 30:03our actual absence networks public,
- 30:05and along with the associated code.
- 30:08Because we're interested in mechanism,
- 30:09of course,
- 30:10very interested in anatomical overlap
- 30:12between the cocaine and obeah networks.
- 30:14However,
- 30:14Interestingly,
- 30:15only compare edges across networks,
- 30:16we actually saw very little overlap,
- 30:18so less than 1% of edges shared.
- 30:20So here on your left we have edges their
- 30:22common to both cocaine and opiate networks,
- 30:25with the red lines indicating shared
- 30:26positive edges and the blue lines
- 30:28indicating shared negative edges.
- 30:29And as you can see,
- 30:31there's only a total of 8 shared edges.
- 30:33Overall,
- 30:34I realized I left off the figure legend here.
- 30:36So just for reference,
- 30:37the positive shared edges include
- 30:39a prefrontal, prefrontal,
- 30:40and limbic connections,
- 30:40as well as a subcortical to parietal
- 30:42connection and negative straight
- 30:44edges include parietal, still in bed,
- 30:46and supportable connections.
- 30:47In addition,
- 30:48we also identified several edges that
- 30:49have opposite opposite associations
- 30:51with opiate versus cocaine use,
- 30:53so these are edges for which,
- 30:55for example,
- 30:55increased connectivity is a positive
- 30:57predictor of cocaine abstinence,
- 30:58but for which decreased connectivity
- 31:00is a positive character.
- 31:01Opiate abstinence,
- 31:02or vice versa.
- 31:04Cities opposing edges are including
- 31:06connection between prefrontal
- 31:07and cerebellar regions as well as
- 31:09the temporal and vital cortices.
- 31:10And as you can see,
- 31:11there are more opposing edges
- 31:13than consistent edges.
- 31:14So these data together really
- 31:15indicating that the neural
- 31:16substrates of cocaine opiate use
- 31:18disorder may be largely disposable.
- 31:25We've also been looking into the
- 31:26specificity of these networks.
- 31:27Are predicting specific drugs
- 31:29across different brain states,
- 31:30so the data I just showed you
- 31:32indicated the cocaine obit network
- 31:33have pretty limited anatomical overlap.
- 31:35However one network was
- 31:36driver from reward task data,
- 31:38another was drive from cognitive data
- 31:39and I haven't yet shown you whether
- 31:41the opiate network also related to
- 31:43cocaine abstinence or vice versa,
- 31:45and I also haven't yet presented data
- 31:47to determine whether the relationship
- 31:48between network connectivity and
- 31:50substances is in fact task dependent.
- 31:52Or in other words,
- 31:53whether or not connect me findings
- 31:55hold across brain states.
- 31:56So to answer this question,
- 31:58first we looked at the impact of
- 31:59brain state on network identification.
- 32:01So we repeat our connectome based
- 32:03model of opiate abstinence using
- 32:05reward instead of cognitive task data,
- 32:06and we find that as I just showed you,
- 32:09we are able to predict opiate abstinence
- 32:11using cognitive control house data.
- 32:13However,
- 32:13we're not able to predict opiate
- 32:15abstinence using reward task data.
- 32:16Similarly,
- 32:17when we repeat our model of cocaine
- 32:19abstinence using cognitive data
- 32:20instead of reward task data.
- 32:22We find that we're only able to
- 32:24identify a cocaine network using
- 32:25the reward task data,
- 32:27demonstrating the identification
- 32:28of both obit cocaine abstinence
- 32:30networks was brain state specific.
- 32:31So having established that next we
- 32:33wanted to test whether once identified
- 32:36relationships between networks and
- 32:38specific substances might hold or
- 32:40generalize across brain states.
- 32:42To her on the left,
- 32:43with the Association between
- 32:44the in treatment,
- 32:45opiate abstinence and connectivity
- 32:47within the cocaine network,
- 32:48and as you can see,
- 32:49there's no relationship and on the right
- 32:51we have the relationship between cocaine,
- 32:53network connectivity and abstinence
- 32:54across different brain states.
- 32:55So for these analysis we're taking
- 32:57the cocaine cocaine network that were
- 32:59identified using reward task performance,
- 33:00using it as a mask to extract
- 33:02connectivity during cognitive task
- 33:04performance and also during resting
- 33:05state and what we find is that we
- 33:07again see a modest linear relationship
- 33:09between network connectivity and
- 33:10within treatment cocaine abstinence.
- 33:12So together these data indicate that all
- 33:14cocaine network does not generalize.
- 33:16Predict opiate abstinence,
- 33:17the relationship between cocaine,
- 33:19network connectivity and cocaine abstinence
- 33:21does in fact generalize across brain states.
- 33:24We see a very similar
- 33:25pattern with opiate network,
- 33:27so on the left we have the
- 33:28Association between within treatment,
- 33:30cocaine,
- 33:30abstinence and connectivity,
- 33:31even though be it network.
- 33:32This time during cognitive task
- 33:34performance and you can see that
- 33:35there is no significant Association
- 33:37where we take the network that we
- 33:39identified using cognitive task data.
- 33:40Use it as a mask to extract
- 33:42connectivity during reward task
- 33:43performance or during resting state.
- 33:45We again see a positive Association between
- 33:47network connectivity and within treatment.
- 33:49Abstinence would be absence.
- 33:51Again,
- 33:51indicating that while the opiate network
- 33:53does not generalize predict cocaine,
- 33:55use the relationship between
- 33:57opioid network connectivity and
- 33:59opiate abstinence does generalize
- 34:01across brain states.
- 34:02So I don't want to overly
- 34:04believer this point,
- 34:04but this is just to show
- 34:06you that we've run
- 34:07all possible combinations of this.
- 34:08So, for example, we've looked at whether
- 34:10opiate network connectivity during resting
- 34:11state relates to cocaine abstinence,
- 34:13and in all cases the specificity
- 34:14of these effects remains.
- 34:15So. In other words,
- 34:16we're seeing a double dissociation
- 34:18such that the cocaine network is
- 34:19consistently unrelated opiate use,
- 34:20and vice versa.
- 34:23As of the cocaine data,
- 34:24we've looked into the relationship
- 34:26between connectivity within the opiate
- 34:28network at post treatment and subsequent
- 34:30opiate use at 6 month follow up and
- 34:32we again find a similar relationship
- 34:33between post treatment activity
- 34:35and abstinence following treatment,
- 34:36potentially indicating some consistently
- 34:38this relationship overtime.
- 34:39And when we compare connectivity
- 34:40within the opiate network from
- 34:42free to post treatment,
- 34:43we also don't see any significant changes,
- 34:44further suggesting stability
- 34:46of these effects.
- 34:47Which is interesting to me,
- 34:49touches on an important point,
- 34:51which is that networks contributing
- 34:52to treatment response may in fact
- 34:54be distinct from those that change
- 34:56with treatment or that are directly
- 34:58implicated in disease pathology.
- 35:00So for example,
- 35:01brain regions for Tim's treatment
- 35:03responses and other disorders such as
- 35:05depression often have limited overlap
- 35:07with regions consistently found to
- 35:09differentiate patients from controls.
- 35:11Another possibility is the brain
- 35:12regions that predict treatment
- 35:13outcomes may just be different from
- 35:15those that change with treatment,
- 35:16and so at first I know that
- 35:18sounds counter intuitive,
- 35:18but when you think about it
- 35:20in a clinical context,
- 35:21we know that factors that
- 35:22predict treatment response.
- 35:23So for example,
- 35:24motivation to change can be distinct
- 35:25from those that change with treatment
- 35:27such as the acquisition of new skills.
- 35:29Thus the same may be true
- 35:31for neural networks.
- 35:32Further,
- 35:32it's possible that changes with
- 35:34an absence networks may take
- 35:35time to emerge and may only be
- 35:37detectable months after treatment,
- 35:38and that theory is consistent with
- 35:40data demonstrating the abstinence
- 35:41rates continue to improve following
- 35:43some behavioral treatments
- 35:44for cocaine use disorder.
- 35:45Thus,
- 35:45it stands to reason that the
- 35:47same may be true for the brain.
- 35:49Neural change may just take time to emerge.
- 35:53And so we began trying to
- 35:55explore that first possibility.
- 35:56The possibility that our networks are not
- 35:58only predictive of future substance use,
- 35:59but that they also just may be altered
- 36:01relative to healthy control individuals,
- 36:03and therefore perhaps linked to
- 36:05addiction pathophysiology more generally.
- 36:07Theoretical basis for looking into
- 36:09comparisons with healthy controls
- 36:10comes from really nicely by Hugh
- 36:12Garavan and colleagues from a few
- 36:14years ago in which they propose
- 36:15a couple of different scenarios
- 36:16for what prolonged absence might
- 36:18look like at the brain level.
- 36:20So here at the top,
- 36:21they propose that one scenario would
- 36:23be that recovery from addiction could
- 36:24involve some sort of restoration of
- 36:26premorbid brain function in the middle,
- 36:28they propose that an alternative
- 36:30hypothesis could be that prolonged
- 36:31recovery may in fact require some
- 36:33sort of hyper functionality.
- 36:34Brain regions involved in absence
- 36:36maintenance to above the level
- 36:38observed in healthy controls.
- 36:39Or finally,
- 36:40a third option at the bottom is the
- 36:42individuals in recovery from addiction may
- 36:44continue to exhibit decreased function,
- 36:46thereby confirming vulnerability for relapse.
- 36:47So just unpack that a bit
- 36:49more based on this model.
- 36:50If we were to compare absence
- 36:52networks to healthy controls and
- 36:53we found that absent individuals
- 36:55had similar levels of network
- 36:56strength relative healthy controls,
- 36:58we might conclude that what we're seeing
- 37:00is a return to premorbid functioning.
- 37:03Alternatively,
- 37:03if we were to find the individuals
- 37:05who achieve abstinence have increased
- 37:06network strength relative to controls,
- 37:08we might conclude that we're
- 37:10seeing is an elevation of brain
- 37:12function or a hyper recovery.
- 37:13And finally,
- 37:14if we were to find the abstinent
- 37:16individuals at decreased network
- 37:17strength relative to controls,
- 37:18we might interpret that as indicating
- 37:21continued vulnerability for relapse.
- 37:22So to test these theories,
- 37:24we've computed connectomes from
- 37:25identical task data for 38 age
- 37:27and sex matched non substance
- 37:28using control participants.
- 37:29And we've compared this to our
- 37:31Poly substance using sample.
- 37:32So here I'm just showing your binarization
- 37:34of the data I present earlier.
- 37:36So we have cocaine network sent
- 37:37for individuals to achieve
- 37:39some abstinence from cocaine.
- 37:40Drug treatment on the left and network
- 37:42show and for individuals who did
- 37:44not achieve absence on the right.
- 37:46And when we add in our controls what
- 37:47we find is it non substance using
- 37:49individuals or actually intermediary
- 37:51between responders and nonresponders.
- 37:52Such that our treatment responders
- 37:54actually have increased network
- 37:56strength relative to controls and
- 37:57our non responders have decreased
- 37:59network strength relative to controls.
- 38:00We again seem very similar pattern
- 38:02when we look at our opiate network,
- 38:04we find that our control group
- 38:05is again intermediary between
- 38:06responders and nonresponders with
- 38:08responders have increased network
- 38:09strength relative to controls.
- 38:10Although the difference between
- 38:12the controls and the non
- 38:13responders here isn't significant.
- 38:15So for both the cocaine and opiate networks,
- 38:17we're seeing this powder in the treatment
- 38:19responders have greater network,
- 38:20strengthen control participants.
- 38:22Again,
- 38:22consistent with this notion of an
- 38:24elevation of function relative to
- 38:26controls or hyper functionality.
- 38:28Most recently we've been applying
- 38:29this same approach to try to identify
- 38:31predictors of cannabis use outcomes,
- 38:33which I think is a really important issue,
- 38:35particularly within the context of the
- 38:38changing legislation in this country.
- 38:40Started my talk.
- 38:41I really focused on cocaine and
- 38:43opiates and I referenced increased
- 38:44overdose kristallen treatment as a
- 38:46significant motivator for identifying
- 38:48brain based predictors outcomes.
- 38:50And so while cannabis of course
- 38:52poses much less of an overdose risk,
- 38:54assuming it hasn't been mixed with
- 38:56another illicit substance cannabis use,
- 38:57none of nonetheless remains
- 38:59extremely prevalent,
- 38:59so approximately 15% of you as adults
- 39:02reported past year use back in 2017,
- 39:04and that number is likely higher
- 39:05now given all the ongoing changes
- 39:07to legislation in this country.
- 39:09So not only is cannabis use
- 39:11potentially becoming more prevalent,
- 39:12it's also becoming stronger,
- 39:14so the blue line here corresponds to
- 39:16the proportion of THC in cannabis
- 39:18samples over the past 20 years or so.
- 39:20And the green line corresponds
- 39:22to the portion of CVD,
- 39:23and as you can see,
- 39:24cannabis is becoming significantly
- 39:26stronger or composed of a significantly
- 39:28higher ratio of THC relative to CD.
- 39:30For these analysis we're
- 39:31combining aggregate pretreatment
- 39:32data from 2 separate arctis,
- 39:34one led by Kathy Carroll,
- 39:35another led by Brian Killick,
- 39:37and by combining data from these two samples,
- 39:39we end up with Nora merging data
- 39:41that needs our quality control
- 39:42criteria for 58 individuals of
- 39:44primary cannabis use disorder.
- 39:48For these analysis we again have
- 39:50connectivity matrices that we generate
- 39:52from both cognitive control and reward
- 39:53task data which we enter interpretive
- 39:55model along with their dimensional
- 39:57biological measure of abstinence. Surprise.
- 40:00What we find is that we're able to
- 40:02generate accurate predictive models of
- 40:04cannabis abstinence using both data types,
- 40:06suggesting that both cognitive and
- 40:07reward related brain states are relevant
- 40:09for understanding cannabis abstinence.
- 40:11So here, on the left we have the
- 40:13correspondence between predicted
- 40:14and actual abstinence values for
- 40:15the cognitive tasks shown in Gray,
- 40:17and for the reward tasks shown in white.
- 40:20And what we can see is that for
- 40:22both types of data,
- 40:23the spearings was hovering just under .4,
- 40:25indicating that about 16% of the
- 40:27variance and within treatment cannabis
- 40:29abstinence can be accounted for by
- 40:31connectivity within these networks.
- 40:32As with the cocaine and opiate networks,
- 40:34we've also tested the generalizability
- 40:35of this effect to an independent
- 40:37sample of individuals.
- 40:38The primary cocaine use disorder,
- 40:39the results of which are shown on your right,
- 40:42and as you can see,
- 40:43we found that the cannabis network
- 40:45does not generalize further,
- 40:46indicating substance specificity of
- 40:48our different abstinence networks.
- 40:50We're only just starting to dig down
- 40:52into the anatomy of these network,
- 40:54but so far the anatomy of the networks
- 40:56identified during reward and cognitive
- 40:58tasks do have some similarities,
- 40:59so these cord plots are summarizing
- 41:01positive network connections for the
- 41:03network identified during cognitive
- 41:04task performance on your left and
- 41:06reward task performance on your right.
- 41:08So these plants are really similar
- 41:10to the other circle plots are
- 41:12presented previously,
- 41:13but here,
- 41:14instead of summarizing macroscale
- 41:15regional connectivity there,
- 41:16summarizing connectivity between
- 41:17Canonical networks and we can see that
- 41:20for both tasks the cannabis network
- 41:21is characterized by high degrees of
- 41:23connections between frontal parietal and
- 41:25motor sensory networks as indicated by
- 41:27these sort of blue to pink arcs here.
- 41:30But we also see some differences.
- 41:32For example,
- 41:33we see differences in patterns of
- 41:35connectivity related to salience
- 41:36and visual networks.
- 41:37These differences are more striking.
- 41:39When we compare the negative
- 41:40connections so connections to which
- 41:42decreased connectivity is positive,
- 41:43productive cannabis abstinence.
- 41:44So here we're seeing clear differences
- 41:46in patterns of connectivity between
- 41:48salience and motor sensory networks,
- 41:49characterized by these sort of
- 41:51phase to pink arcs.
- 41:53And we can also see that joint
- 41:55cognitive task performance.
- 41:56The model is identifying a number
- 41:58of connections between the default
- 41:59mode and frontal network as shown
- 42:01by the screens beige dark,
- 42:02which we're not seeing in the
- 42:04reward task model at all.
- 42:06So as I said, these are brand new data.
- 42:08We're still working on drilling down
- 42:09into the anatomy of these networks.
- 42:11A lot of the work with this stuff comes
- 42:13after you've already done your model.
- 42:15But really,
- 42:16these findings are just again
- 42:17highlighting this idea that brain
- 42:19State may be a significant factor
- 42:20for generation of optimal models.
- 42:22So even if we're trying to predict the
- 42:24same behavior in the same individuals,
- 42:25the connections that we can identify should
- 42:28be partially dependent on the brain state
- 42:30that participant was in during acquisition.
- 42:32That's all the day I wanted to show you
- 42:35today, which I think demonstrates with
- 42:37ability of our approach to generate
- 42:39specific externally valid predictions,
- 42:41but also to provide more biological insight.
- 42:44So hopefully we've demonstrated today
- 42:45is that networks identified using this
- 42:48approach are clinically relevant. That is,
- 42:50there able to predict treatment response.
- 42:52Is there also externally valid or able
- 42:54to generalize product specific behaviors
- 42:56and novel symbols and individuals?
- 42:58In addition, despite their complexity,
- 43:00these networks are in fact
- 43:01biologically meaningful,
- 43:02and that they're composed the specific
- 43:05connections observing specific behaviors.
- 43:07And finally, these networks are a bust.
- 43:09A common sources of variance,
- 43:10so they predict even after
- 43:12controlling for severity treatments.
- 43:13I mentor medication status.
- 43:16If you're interested in applying predictive
- 43:18modeling approaches your own data,
- 43:19I recommend checking out the August
- 43:21issue of biological psychiatry,
- 43:22CNN I,
- 43:22which is dedicated to data driven approaches.
- 43:24This includes our our review paper that
- 43:26covers recommendations for best practices
- 43:27in cross validated biomarker research
- 43:29within the specific context of addictions.
- 43:31However,
- 43:31I think that many of the issues that
- 43:33we cover really applied to clinical
- 43:34predictive modeling more generally,
- 43:36which is probably why I figure summarizing
- 43:38the work for this approach made the cover.
- 43:40So again,
- 43:41if you're interested in these approaches,
- 43:42I suggest that you check that out.
- 43:46Just briefly,
- 43:46I'm not going to go into all
- 43:48the recommendations here,
- 43:49but I do want to just highlight
- 43:51a few key points,
- 43:52one of which is careful consideration
- 43:54of the window of assessment.
- 43:56So while scanning prior to a clinical
- 43:57intervention is often considered desirable,
- 43:59it's important term it's getting during this
- 44:01time can introduce unnecessary confounds,
- 44:03such as effects of acute
- 44:04intoxication or withdrawal.
- 44:05In the case of addiction or mood,
- 44:07state or psychosis.
- 44:08In the case of other disorders.
- 44:10In addition,
- 44:11scanning prior to clinical intervention
- 44:13may not be feasible in this treatment.
- 44:15Initiation is delayed,
- 44:16which may place unnecessary burden
- 44:18on the patient.
- 44:19I also want to emphasize the importance
- 44:21of employing multiple performance
- 44:22metrics for quantifying model accuracy,
- 44:24which I didn't go into too much today.
- 44:27But again,
- 44:27it's interview paper and also
- 44:29important of setting realistic
- 44:30expectations for effect size
- 44:32estimates when you're talking about
- 44:34results from cross validated models.
- 44:36Finally,
- 44:36I want to emphasize the importance
- 44:38of conducting post hoc testing to
- 44:40provide elucidation of mechanism,
- 44:41as without this findings or models,
- 44:43even with high accuracy,
- 44:44can do very little to advance our
- 44:47understanding of the underlying neurobiology,
- 44:49which is essential for informing
- 44:51novel treatment development.
- 44:52So in this context,
- 44:53it's important to consider model findings
- 44:55across multiple levels of interpretation,
- 44:57with the most basic level being the
- 44:59connections themselves and the most
- 45:00abstract level being the overarching
- 45:02or biological model which can be
- 45:03used to guide treatment and act
- 45:05as a basis for further testing.
- 45:08Within the context of a region
- 45:09of interest of region
- 45:10of interest based approach,
- 45:12one simple method of determining
- 45:13significance of different features.
- 45:15So significance of individual regions
- 45:16of interest or networks is to just rerun
- 45:19the model excluding specific features.
- 45:20This is that sort of virtual
- 45:22lesion approach that I talked
- 45:23about earlier with Soviet network.
- 45:25And we do this in order to determine
- 45:28which features were necessary
- 45:29for optimal model performance.
- 45:31So for example, to ask the question,
- 45:34does a middle volume contribute
- 45:36to overall model performance?
- 45:38Similarly, we can rerun the model
- 45:39only including selected features,
- 45:41and that will enable determination of the
- 45:42relative weight of specific features.
- 45:44So that would answer the question
- 45:46what is the predictability of
- 45:48amygdala volume by itself,
- 45:49and so these are really simple steps,
- 45:51but people often skip over them,
- 45:53so this is just a reminder that elucidation
- 45:56should also be a goal of prediction.
- 45:58We have several ongoing
- 46:00projects to extend this work,
- 46:01including our one focusing on
- 46:02neural markers of opiate use
- 46:04during the postpartum period,
- 46:05among women receiving methadone,
- 46:06we also have a pilot project which
- 46:08we're using real time fMRI to try
- 46:10to directly target connectivity
- 46:11within that will be at network,
- 46:13so we're interested in whether these
- 46:15connections are in fact modifiable,
- 46:16and we also have some medication.
- 46:18Studies were looking into that too.
- 46:21A sort of separate side of my
- 46:23lab work is also developmental,
- 46:25so we've also been using a
- 46:26connectome based approach to identify
- 46:28developmental mechanisms initiation,
- 46:29which is an ongoing collaboration
- 46:31with Dustin and Godfrey.
- 46:32Pearls in here at Yale,
- 46:33but also with Mary Heitzig in Hugh Garavan.
- 46:37There's also other studies that
- 46:39we wanted were not funded for yet,
- 46:41so we just received a very promising
- 46:42initial score from another or one
- 46:44focusing on developmental mechanisms
- 46:46of initiation, but this time,
- 46:47using the ABCD data set and including
- 46:49considerations ivkova 19 related factors,
- 46:51and that's a collaboration with
- 46:52Danilo Stockem ago.
- 46:53Finally,
- 46:53we're very interested in extending
- 46:55our work across different scanners in
- 46:57clinical settings and also interested
- 46:58in looking into potential sex
- 47:00differences in it will be at Nora markers,
- 47:02and so we've recently applied for
- 47:04funding to pursue that works,
- 47:05so fingers crossed on that one.
- 47:08I'll end there, but I want to just
- 47:10thank all of my collaborators,
- 47:11and because of the coauthors on the
- 47:13data presented today, and also my Cal,
- 47:15my mentor is Kathy Carolyn,
- 47:16Mark, but Enzo, also,
- 47:17of course,
- 47:17when I think everyone in my lab
- 47:18and all of the funders who make
- 47:20this research possible and thanks,
- 47:22all of you for listening.
- 47:26Thank you so much Sarah.
- 47:28We can we have a little time
- 47:30if there are any questions.
- 47:33It was a really great talk and
- 47:35very sophisticated systematic
- 47:36work that you've been doing.
- 47:40Thank you.
- 47:50Chris, go ahead.
- 47:52Yes Sarah great great talk.
- 47:53Thank you in your quick summary
- 47:55of the predictive modeling.
- 47:57You emphasize that you're
- 47:58looking at linear relationships.
- 48:01And the computational
- 48:02accessory for that is clear,
- 48:04but it also may be limiting
- 48:06because you may have threshold
- 48:07or other non linear relationships
- 48:09that are equally important.
- 48:10Or more important is there is it possible
- 48:13to incorporate nonlinear relationships?
- 48:14Or does the parameter space just blow
- 48:16up so quickly that that can't be
- 48:18done? That's a great question.
- 48:20I believe that Dustin Shine
- 48:22Host is working on some of that.
- 48:24I mean, you could use a kernel or
- 48:26something like that. Absolutely.
- 48:28I mean there's no reason why you couldn't.
- 48:30I mean, again, I find the interpretation
- 48:32even just with the linear so hard
- 48:35if we're talking about mechanistic
- 48:37understanding, but absolutely,
- 48:38especially if you have data that aren't.
- 48:40Yeah, yes, that would be great.
- 48:42We should do that next.
- 48:45Well, it enter related question
- 48:47that motivated that question.
- 48:48Is your predictions the amount of
- 48:50variance that you're explaining? You're
- 48:51getting up to 16% or something that's
- 48:54clearly impressive and validates that
- 48:55you're looking at something real.
- 48:57But it's a long way from
- 48:59clinical utility, absolutely.
- 49:00And so I was wondering whether incorporating
- 49:02nonlinear relationships would be a
- 49:04way to bump that up more quickly,
- 49:06but I wonder what other thoughts
- 49:08you have about what the future steps
- 49:11in this line of research might be.
- 49:13That will get us to. 85%.
- 49:17Whatever the threshold may
- 49:19may need to be for this to become clinical,
- 49:22clinically actionable?
- 49:22Yeah, absolutely, that's a great question.
- 49:24I mean, I think. You know,
- 49:26so we've done some stuff where
- 49:28we've been for the cocaine data in
- 49:30the external replication example,
- 49:32where we combine in other clinical features.
- 49:34So if we just add in, you know.
- 49:36So we take the network strength,
- 49:38but then we add in baseline
- 49:40severity or past month cocaine use,
- 49:41which are things that by themselves
- 49:43aren't sufficient to predict outcome.
- 49:44We do find that the model accuracy improves,
- 49:47so when we do that, if we're doing,
- 49:49just say yes, no any absence from treatment,
- 49:51we can get up to 71% accuracy.
- 49:53But again, this all needs to be validated
- 49:55in different clinical settings.
- 49:57And and all that stuff like this is,
- 49:59you know, and if.
- 50:00Magnets, so the multisite application
- 50:02that I mentioned at the end,
- 50:04I'm really excited about and I think
- 50:06it's sort of the next step to try to
- 50:09understand if these things are real,
- 50:11so we'll see what their viewers think.
- 50:15Thanks.
- 50:16Thinking.
- 50:18He said that was beautiful
- 50:19and really convincing data,
- 50:21especially the way you generalized
- 50:22across those discrete patient samples.
- 50:24And So what I was curious about is if
- 50:27you could have any task you wanted in
- 50:29the scanner because you're using these
- 50:31cognitive control and reward tasks
- 50:33that are sort of proxies for constructs,
- 50:35do you think are relevant and you've
- 50:37shown variants with different task states?
- 50:39What do you think the ideal
- 50:41task would be for prediction?
- 50:44So it's possible the
- 50:45answer might be drug use,
- 50:46and I'm not a huge huge
- 50:48cue reactivity person,
- 50:49but I think that that could be the answer.
- 50:52At least an opiate use disorder,
- 50:53but the clinics that I work with
- 50:55tend not to want us to show drug use,
- 50:58and so I don't know that that's
- 51:00necessarily the best.
- 51:01I mean,
- 51:02one thing that I think would be really cool,
- 51:04and it Brian Killick and Kathy Carolyn.
- 51:06I've been talking about is so the
- 51:08type of behavioral therapy that we
- 51:10were receiving in the Poly substance
- 51:12using sample is this computerized
- 51:13CBT that Kathy Carroll developed.
- 51:15And we think it would be really cool
- 51:17to scan patients while they're viewing
- 51:19the computerized CBT and so then see
- 51:21if that is a better predictor of treatment.
- 51:23Response to CDT itself, right?
- 51:25So if you get them in the
- 51:28brain state of therapy?
- 51:30So yeah,
- 51:30lots of different directions to go,
- 51:33thanks.
- 51:44Any other questions? Comments.
- 51:50Seen a few things in the chat saying what
- 51:53a wonderful talk it's been, Sarah. Oh yeah.
- 52:02OK, well if there are no other
- 52:04comments will close for today and
- 52:06thank you so much for a really
- 52:08terrific presentation and stimulating.
- 52:10And I'm sure this will lead to some
- 52:13additional collaborations with your
- 52:15colleagues here at Yale and elsewhere.
- 52:17So thank you.