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Cognitive Regime Shift II - When/why/how the Brain Breaks

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Category: Application Area Application Area: Aging Brain

Date/Time: November 12, 2019 - November 13, 2019


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Organizers

  • Steven Petersen (Washington Univ.-St. Louis)

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    Attendees

    Agenda/Schedule
    Click each agenda item's title for more information.
    Tuesday, November 12, 2019
    8:15 am - 8:30 am Day 1 Shuttle Departing Hotel Santa Fe (at lobby) to SFI
    8:30 am - 9:00 am Day 1 Continental Breakfast
    9:00 am - 9:30 am Introductory Remarks - David Krakauer (SFI), Steven Petersen (Washington Univ.-St. Louis), John Krakauer (Johns Hopkins Univ./SFI)
    9:30 am - 10:30 am Collective Computation and Critical Transitions - David Krakauer (SFI)
    10:30 am - 11:30 am Robustness of Brain Function - Nihat Ay (Max Planck Institute/SFI)
    11:30 am - 12:30 pm Task-performing neural network models enable us to test theories of brain computation with brain and behavioral data - Nikolaus Kriegeskorte (Columbia Univ.)
    12:30 pm - 1:30 pm Day 1 Lunch
    1:30 pm - 4:30 pm Round Table Discussion 1: The nature of compensation and cognitive reserves

    Each round table discussion will start with self-introductions of participants listed below. The self-introductions should include how the questions participants proposed prior to the meeting (see p.3-5) map onto the round table topic.

    Nihat Ay (Max Planck/SFI)
    Roberto Cabeza (Duke Univ.)
    Randy McIntosh (Univ. Toronto);
    John Krakauer (Johns Hopkins/SFI);
    4:30 pm - 5:00 pm Day 1 wiki platform work time
    5:15 pm Day 1 Shuttle Departing SFI to Hotel Santa Fe
    7:30 pm (Optional) SFI Community Lecture at the Lensic Performing Arts Center by Melanie Mitchell: Artificial Intelligence: A Guide for Thinking Humans

    Note that Melanie will be signing her new book with the same title at 6:15 - 7:15 PM in the Lensic lobby; the lecture can also be streamed live via SFI's YouTube page and the SFI Twitter page

    Wednesday, November 13, 2019
    8:30 am - 9:00 am Day 2 Continental Breakfast
    9:00 am - 9:30 am Recap from Day 1
    9:30 am - 12:30 pm Round Table Discussion 2: The multiple scales of damage – from cells to networks

    Each round table discussion will start with self-introductions of participants listed below. The self-introductions should include how the questions participants proposed prior to the meeting (see p.3-5) map onto the round table topic.

    Sidney Redner (SFI)
    Steve Petersen (WA Univ. – St Louis);
    Jacopo Grilli (ICTP);
    Richard Frackowiak (Ecole Polytech);
    Dietmar Plenz (NIH);
    Jack Gallant (UC Berkeley);
    Artemy Kolchinsky (SFI)
     
    12:30 pm - 1:30 pm Day 2 Lunch
    1:30 pm - 4:30 pm Round Table Discussion 3: Models for transforming circuits (neural) into tasks (psychology)

    Each round table discussion will start with self-introductions of participants listed below. The self-introductions should include how the questions participants proposed prior to the meeting (see p.3-5) map onto the round table topic.

    Russ Poldrack (Stanford Univ.);
    Viktor Jirsa (Aix-Marseille Univ.);
    Caterina Gratton (Northwestern Univ.);
    Paul Garcia (Columbia Univ.);
    Nikolaus Kriegeskorte (Columbia Univ.)
    David Krakauer (SFI)
    Ehren Newman (Indiana Univ.)
    Susan Fitzpatrick (JSMF)
    4:30 pm - 5:00 pm Day 2 wiki platform work time
    5:15 pm Cocktail
    6:00 pm - 7:30 pm Group dinner
    7:30 pm Day 2 Shuttle Departing SFI to Hotel Santa Fe

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    Abstracts by Presenters

    Nihat Ay (Max Planck Institute/SFI) - Robustness of Brain Function

    The presentation will review core concepts of a theory of network robustness, initially proposed together with David Krakauer. This theory is concerned with the robustness of function, for instance brain function, with respect to structural perturbations. It suggests design principles and adaptation mechanisms for the maintenance of function. The relevance of the theory in relation to brain architectures will be outlined. In particular, the trade-off between parsimony and robustness in motor control will be discussed, thereby drawing connections to the field of embodied intelligence.

    Nikolaus Kriegeskorte (Columbia Univ.) - Task-performing neural network models enable us to test theories of brain computation with brain and behavioral data

    The goal of computational neuroscience is to find mechanistic explanations of how the nervous system processes information to support cognitive function and behavior. Deep neural networks (DNNs), using feedforward or recurrent architectures, have come to dominate several domains of artificial intelligence (AI). As the term “neural network” suggests, these models are inspired by biological brains. However, their units are rate-coded linear-nonlinear elements, abstracting from the intricacies of biological neurons, including their spatial structure, ion channels, and complex dentritic and axonal signalling dynamics. The abstractions enable DNNs to be efficiently implemented in computers, so as to perform complex feats of intelligence, ranging from perceptual tasks (e.g. visual object and auditory speech recognition) to cognitive tasks (e.g. language translation), and on to motor control tasks (e.g. playing computer games or controlling a robot arm). In addition to their ability to model complex intelligent behaviors, DNNs have been shown to predict neural responses to novel sensory stimuli that cannot be predicted with any other currently available type of model. DNNs can have millions of parameters (connection strengths), which are required to capture the domain knowledge needed for task performance. These parameters are often set by task training using stochastic gradient descent. The computational properties of the units are the result of four directly manipulable elements: (1) functional objective, (2) network architecture, (3) learning algorithm, and (4) input statistics. The advances with neural nets in engineering provide the technological basis for building task-performing models of varying degrees of biological realism that promise substantial insights for computational neuroscience.  

    Post-meeting Reflection by Presenter

    Steven Petersen (Washington Univ.-St. Louis) Link to the source page

    I found NIkos's presentation enlightening and on point. I will certainly go to his primer article. I think the imbalance between abstraction and empiric work was strong. There was an uncomfortable level of abstraction for me. I am not sure my perspective has changed too much, because of this. I would have liked to spend more time on the questions Russ raised at the end regarding ways to "get things together".

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    David Krakauer (SFI) Link to the source page

    Much of the emphasis was placed on describing the necessary basic principles, models or data, for describing brain functions.

    These included:

    1. Resting state correlations from imaging data
    2. Behavioral psychological experiments
    3. Local field potentials
    4. Deep neural networks
    5. Information theoretic formalisms.

    Much emphasis was placed on either justifying or discovering appropriate levels for prediction and explanation. On this topic;

    1. Is there a preferred level based on fundamental principles?
    2. How to reconcile computational models (with strong time separation) with dynamical systems models (with a spectrum of time scales)
    3. How to present and justify theoretical frameworks with many free parameters - theory for complex systems (in contrast to mere complication as in physics).
    4. How to triangulate among levels of description

    My own question dealt with the general problem: does the fact of the brain as a computational organ imply distinct regularities in the way in which it breaks?

    One approach to this would be to ask about:

    1. Robustness and adaptability
    2. Critical transitions: order disorder regimes
    3. Cascading failure and percolation.

    This triplet provides a possible informal coordinate system in which to situate a system to include the brain. The rather unique scale and connectivity and general function of brain might suggest that it sit near a critical point, balanced between robust and adaptive regimes.

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    Post-meeting Reflection by Non-presenting Attendees

    Jacopo Grilli (ICTP) Link to the source page

    I am stuck with a picture of aging and collapse, motivated by catastrophic shifts in ecology, which simply takes the form of a saddle-node bifurcation. A functional and dysfunctional system are separated by some energy barrier. Aging (somewhat by definition) corresponds to decreasing energy barrier height (and therefore increasing probability of transition). This (at this level tautological) view comes with two interesting consequences:

    - (critical) slowing down: the typical timescale at which fluctuations relax increases over time

    - in multidimensional system there is an effective one dimensional trajectory describing collapse

    The latter point, suggests high reproducibility in collapse trajectories. At what scale this framework is useful is unclear to me. At the coarser scale, when only two states exist (functional and not functional) the only thing that matters is transition probability (the when, and there is no how and why). At that scale bridges and brains fail in the same way (as lifetime distributions sort of match). I am very confused about the confusion around the scale(s) at which we want to study aging and breaking of brains.

    I found extremely interesting the discussion of machine learning / neural networks as toy models of representation and/or learning in brains.

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    Caterina Gratton (Northwestern Univ.) Link to the source page

    I started my section with the premise that when we discuss the brain "breaking", we often operationalize this in terms of changes in complex behaviors, and that these behaviors may be subserved by large-scale systems of the brain. Much of the work to date has focused on the typical average structure of these systems. I showed some recent work we've done aimed at moving these analyses to the individual level, and discussed some observations we've made based on this.

    (1) I showed that functional network measurements (at rest) can be quite reliable even in single individuals, given enough data

    (2) I showed some data demonstrating that functional network measurements are dominated by stable factors including group commonalities and individual features. Task-state and day-to-day variability is also present, but much smaller in scale.

    (3) I discussed our characterizations of punctate locations of individual differences in functional networks, showing that these locations are present across repeated recordings, relate to altered function, and individuals cluster based on the forms of variants they exhibit. While these individual differences explain some (gross) behavioral differences, the variance they explain is very small. I left off with a question to the group of why this might be: why do we see relatively stable behavior in the face of some large individual differences in brain organization.

    Discussion centered on how we might think about these effects in the context of distributed organization (or not), to what extent these effects can be overcome by functional alignment that does not assume spatial correspondence, and whether manifolds might be a way of modeling variation in brain function that can lead to a similar functional outcome. We also discussed whether behavior has been measured well enough yet, or if we've been too non-specific in our functional assessments.

    General meeting reflection: There were some interesting discussions of multiple different scales and ways of thinking about the brain. I would have liked to have seen a little more cross-talk integration, and/or thoughts about practical directions on which to move forward. How can we better unite models with data? What are the right types of data to collect and theories to test?

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    Dietmar Plenz (NIH) Link to the source page

    HIghlights:

    Hard to prioritize as every talk expanded my perspective and triggered new associations.

    I enjoyed two talks in particular – David’s introduction into ‘breaking’ which provided are nice meta-overview into brain dysfunction outside the usual context of development and aging. Refreshing and lots of food for thoughts.  The triade of ‘breaking/perturbation, critical transition, and cascading failure’ nice transitioned into three more concrete directions, which I would loved to have explored more in that workshop:

    ‘breaking = scale of anatomy’

    ‘critical transition = brain state’

    ‘cascading failure = developmental disturbances’?

    Also very much enjoyed Nikolaus’s overview talk and insight into convolutional deep networks.  Very clear, transparent and a great platform from which discussions emerged.

    My favorite open question:

    What is the computation mechanism/dynamics at the network level ? Move away from correlation analyses.

    Change in perspective: I would like to move away from the discussion of imaging results and move more towards the nature of computation.

    Impact on my own work: Converging ideas on collective decision making and coherence potentials.

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    Reference Materials by Presenting Attendees

    Steven Petersen (Washington Univ.-St. Louis)

    • Gratton et al. 2019 Cereb Cortex.
    Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
    Emergent Functional Network Effects in Parkinson Disease Caterina Gratton, Jonathan M. Koller, William Shannon, Deanna J. Greene, Baijayanta Maiti, Abraham Z. Snyder, Steven E. Petersen, Joel S. Perlmutter, Meghan C. Campbell Cerebral cortex (New York, N.Y. : 1991) 2019 0 3 Download (Encrypted)

    David Krakauer (SFI)

    Flack et al. 2012 summarizes our understanding of mechanisms that generate robustness (invariance of function to non-trivial perturbations) in biological and social systems. It provides a classification of these mechanisms in pursuit of more general principles that confer robustness at different time and space scales. 

    Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
    Robustness in biological and social systems Jessica Flack, Peter Hammerstein, David Krakauer Evolution and the Mechanisms of Decision Making 2012 0 27 Download (Encrypted)
    Reference Materials by Non-presenting Attendees

    Jacopo Grilli (ICTP) Link to the source page

    Podolsky et al find, In the context of regulatory networks and expression profiles, a connection between critical dynamics (the gene regulatory network is at the edge of stability) and aging. This link between criticality (often associated to "functionality" and flexibility) and aging is particularly intriguing also if translated into the context of neural networks and brain diseases.

    Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
    Critical dynamics of gene networks is a mechanism behind ageing and Gompertz law Dmitriy Podolskiy, Ivan Molodtsov, Alexander Zenin, Valeria Kogan, Leonid I. Menshikov, Vadim N. Gladyshev, Robert J. Shmookler Reis, Peter O. Fedichev q-bio.MN 2016 0 2 Download (Encrypted)

    Gagan Wig (UT Dallas) Link to the source page

    In addition to bridging genetic and cellular to the cognitive and behavioral levels, an examination and integration of broader levels of complexity can further our understanding of when/why/how the brain breaks. I propose that this can be achieved by understanding how an individual’s lifestyle and environment relate to their resilience and vulnerability to brain decline.

    I’m sharing a story (D. Buettner, NY Times, 2012) that begins to describe how multiple complex systems (including social, cultural, physiological, technological) may be important to consider for thinking about the health and robustness of an individual. I’m also sharing an article (Chan et al., PNAS, 2018) that summarizes my lab’s first attempt at integrating methods that examine an individual’s psycho-social environment with measures of their brain network organization to begin to understand the types of features that may lead to variability in brain network aging.

    Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
    The Island Where People Forget to Die - The New York Times New York Times Magazine 2012 0 3 Download
    Socioeconomic status moderates age-related differences in the brain’s functional network organization and anatomy across the adult lifespan2 Proceedings of the National Academy of Sciences 2018 0 0 Download
    Download

    Caterina Gratton (Northwestern Univ.) Link to the source page

    • Warren et al. 2014 discusses a case where network models of the brain may help to provide information about behavioral disruptions after brain damage.
    • Gratton et al. 2018 reviews aspects of the forms of variation available in functional MRI measurements, which may constrain which types of questions different fMRI measures are best suited to addressing.
    Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
    Network measures predict neuropsychological outcome after brain injury David E. Warren, Jonathan D. Power, Joel Bruss, Natalie L. Denburg, Eric J. Waldron, Haoxin Sun, Steven E. Petersen, Daniel Tranel Proceedings of the National Academy of Sciences of the United States of America 2014 0 3 Download
    Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation Caterina Gratton, Timothy O. Laumann, Ashley N. Nielsen, Deanna J. Greene, Evan M. Gordon, Adrian W. Gilmore, Steven M. Nelson, Rebecca S. Coalson, Abraham Z. Snyder, Bradley L. Schlaggar, Nico U.F. Dosenbach, Steven E. Petersen Neuron 2018 0 4 Download

    Dietmar Plenz (NIH) Link to the source page

    • Meisel et al. 2017 demonstrates that sleep deprivation associated with rapid cognitive decline correlates with a deviation from critical dynamics quantified in the change in long-term temporal correlations or critical slowing down.
    • Seshadri et al. 2018: using an animal model for schizophrenia, it is shown that a hallmark of the disease – loss of working memory – correlates with deviation from avalanche dynamics. Memory performance and critical dynamics can be acutely rescued with the NMDA receptor agonist D-serine.
    Title Author name Source name Year Citation count From Scopus. Refreshed every 5 days. Page views Related file
    Neuronal avalanches and coherence potentials D. Plenz European Physical Journal: Special Topics 2012 0 1 Download (Encrypted)
    Coherence potentials: Loss-less, all-or-none network events in the cortex Tara C. Thiagarajan, Mikhail A. Lebedev, Miguel A. Nicolelis, Dietmar Plenz PLoS Biology 2010 0 1 Download (Encrypted)
    Decline of long-range temporal correlations in the human brain during sustained wakefulness Christian Meisel, Kimberlyn Bailey, Peter Achermann, Dietmar Plenz Scientific Reports 2017 0 3 Download
    Altered avalanche dynamics in a developmental NMDAR hypofunction model of cognitive impairment Saurav Seshadri, Andreas Klaus, Daniel E. Winkowski, Patrick O. Kanold, Dietmar Plenz Translational Psychiatry 2018 0 4 Download

    General Meeting Reference Material