Over the years, the CNBC has evolved naturally. It has been a long time since we have stepped back and considered how well CNBC is serving our community or if we should adjust how we operate to better engage our faculty and trainees.
The ‘Future of CNBC’ Discussion Questions:
|5:00 PM||Check-in for guests with Friday arrival|
|7:00 PM||Pizza, salad, and refreshments will be available for dinner
|7:30 PM||Future of the CNBC Brainstorming Session|
|8:00 PM||Data Blitz|
|8:30 PM||Games: Scientific Telestrations|
McClelland Awards (Student)
|9:45 AM||Jay Hennig (Mentors: Steve Chase & Byron Yu)
Neural Computation, Carnegie Mellon University
Constraints on Neural Redundancy (abstract)
|10:15AM||Casey Roark (Mentor: Lori Holt)
Psychology, Carnegie Mellon University
Perceptual Dimensions Influence Auditory Category Learning (abstract)
|Strick Awards (Postdoc)|
|11:00 AM||Chengcheng Huang (Mentor: Brent Doiron)
Mathematics, University of Pittsburgh
Circuit Models of Low-Dimensional Shared Variability in Cortical Networks (abstract)
|11:30 AM||João Semedo (Mentor: Byron Yu)
Electrical & Computer Engineering, Carnegie Mellon University
Cortical Areas Interact through a Communication Subspace (abstract)
|12:00 PM||Box Lunches will be available in the Grand Ballroom for trainees and Wintergreen for faculty|
|12:00 PM||Lunch / Future of CNBC Discussion
Trainees – Grand Ballroom
Faculty – Wintergreen
|1:00 PM||Future of CNBC discussion presentations
Trainee to Faculty / Faculty to Trainee
|1:45 PM||Panel Discussion – ‘Modeling: What and why’:
What do models explain? Do they explain anything? Or are they just helpful formal/mathematical descriptions, perhaps at best tools for prediction but not illuminating about the brain/mind.
|2:45 PM||Jana Kainerstorfer
Assistant Professor of Biomedical Engineering
Carnegie Mellon University
Talk Title TBA
|3:15 PM||Rebecca Price
Assistant Professor of Psychiatry and Psychology
University of Pittsburgh
Targeting Neuroplasticity Deficits in Anxiety and Depression: Towards Psychobiological Intervention (abstract)
|3:45 PM||Greg Siegle
Associate Professor of Psychiatry, Psychology, and Clinical and Translational Science
University of Pittsburgh
Emotion Waves: From Computational Models to Psychiatric Interventions (abstract)
|4:30 PM||Keynote Speaker
Professor of Psychiatry and Behavioral Sciences
UC Davis MIND Institute
Mouse Models of Autism to Test Hypotheses about Causes and to Discover Effective Treatments (abstract)
|5:45 PM||Dinner / Poster Session|
|7:00 PM – 8:00 PM||Poster Judging
Presenters should be at posters if they want to be judged.
|8:30 PM||Poster Awards|
|7:00 AM – 9:00 AM||Breakfast
Slopeside Dining Room
|9:00 AM – 10:00 AM|| Yoga/Meditation Talk
|10:00 AM – 11:00 AM||Breakfast
Slopeside Dining Room
Jacqueline Crawley, Ph.D
Mouse Models of Autism to Test Hypotheses about Causes and to Discover Effective Treatments
Autism is a neurodevelopmental disorder diagnosed by two categories of behavioral criteria: (1) social interaction and social communication deficits; (2) repetitive behaviors with restricted interests. Strong evidence implicates genetic mutations as primary risk factors. Over 100 risk genes for autism spectrum disorder have been identified over the past decade. Mice with targeted mutations in these many of these risk genes are increasingly available to test hypotheses about genetic causes of autism. How do you model the unique behavioral symptoms of autism in mice? Our laboratory designed mouse behavioral assays with conceptual relevance to the diagnostic and associated symptoms of autism. We employ genetic mouse models with social deficits and repetitive behaviors to discover potential pharmacological therapeutics for autism. This lecture will present strategies and early preclinical results, which may lead to clinical trials of medical interventions to improve the core symptoms of autism.
Constraints on Neural Redundancy
Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation.
Circuit Models of Low-Dimensional Shared Variability in Cortical Networks
Trial-to-trial variability is a reflection of the circuitry and cellular physiology that makeup a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional. Previous model cortical networks cannot explain this global variability, and rather assume it is from external sources. We show that if the spatial and temporal scales of inhibitory coupling match known physiology, networks of model spiking neurons internally generate low dimensional shared variability that captures population activity recorded in vivo. Shifting spatial attention into the receptive field of visual neurons has been shown to differentially modulate shared variability within and between brain areas. A top-down modulation of inhibitory neurons in our network provides a parsimonious mechanism for this attentional modulation. Our work provides a critical link between observed cortical circuit structure and realistic shared neuronal variability and its modulation.
Targeting Neuroplasticity Deficits in Anxiety and Depression: Towards Psychobiological Intervention
Research in psychiatry increasingly emphasizes cross-cutting biopsychosocial factors that are heterogeneous within, and across, discrete psychiatric diagnoses. The promise of this work is that it will generate a process-based framework to improve psychiatric assessment and treatment, but translating neuroscience findings into true advances in clinical care remains a challenge. In this talk, I will propose a broad view of neuroplasticity deficits as an integrative psychobiological construct that may contribute to anxiety and depression across diagnoses and age groups. I will discuss ongoing attempts to translate such findings into mechanistic treatment strategies and personalized treatment prescriptions capable of remediating neurocognitive disruptions and alleviating symptoms. Specific areas of focus within this work include: 1) characterizing neurocognitive processing patterns in affective disorders through behavioral information processing tasks and fMRI; 2) the targeted modification of information processing mechanisms through computer-based training; and 3) clinical studies of intravenous ketamine for depression and suicidality. Future directions will include ongoing studies which focus on developing novel, synergistic, psychobiological treatment combinations to create, and then exploit, neuroplasticity within affective circuits.
Perceptual Dimensions Influence Auditory Category Learning
Human category learning appears to be supported by dual learning systems. Previous research indicates the engagement of distinct neural systems in learning categories that require selective attention to dimensions versus those that require integration across dimensions. This evidence has largely come from studies of learning across perceptually separable visual dimensions, but recent research has applied dual system models to understanding auditory and speech categorization. Since differential engagement of the dual learning systems is closely related to selective attention to input dimensions, it may be important that acoustic dimensions are quite often perceptually integral and difficult to attend to selectively. We investigated this issue across artificial auditory categories defined by center frequency and modulation frequency acoustic dimensions. Learners demonstrated a bias to integrate across the dimensions, rather than to selectively attend, and the bias specifically reflected a positive correlation between the dimensions. Further, we found that the acoustic dimensions did not equivalently contribute to categorization decisions. These results demonstrate the need to reconsider the assumption that the orthogonal input dimensions used in designing an experiment are indeed orthogonal in perceptual space as there are important implications for category learning.
Cortical Areas Interact through a Communication Subspace
Most brain functions involve interactions among multiple, distinct areas or nuclei. For instance, visual processing in primates requires the appropriate relaying of signals across many distinct cortical areas. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Here we investigate how trial-to-trial fluctuations of population responses in primary visual cortex (V1) are related to simultaneously-recorded population responses in area V2. Using dimensionality reduction methods, we find that V1-V2 interactions occur through a communication subspace: V2 fluctuations are related to a small subset of V1 population activity patterns, distinct from the largest fluctuations shared among neurons within V1. In contrast, interactions between subpopulations within V1 are less selective. We propose that the communication subspace may be a general, population-level mechanism by which activity can be selectively routed across brain areas.
Emotion Waves: From Computational Models to Psychiatric Interventions
Affective science often assumes that “normal” functioning is characterized by adaptive reactions to emotional stimuli and subtle regulation to yield a pleasantly dynamic homeostasis. Using a simple computational model I will show that such a state of affairs exists in a very narrow band of parameters that are unlikely to represent most people’s emotional lives. Rather, I will suggest that the models’ behaviors are more consistent with predictions of emotional freakouts and shut-downs, which we document in physiological and neuroimaging studies. I will further suggest that emotional functioning in psychiatric disorders can be captured by minor parameter variations in these models, and that a new generation of treatments accounting for natural wide variation in emotional reactivity has promising effects in early clinical trials.