Feb241:00pm - 2:00pm
Talk: Beyond Convolutional Neural Networks: Differentiable Visual Computing (Tzu-Mao Li, Postdoc, UC Berkeley)Watson Center for Information Technology (CIT)
While convolutional neural networks have become powerful tools for
processing visual data, their inflexibility raises several challenges.
Firstly, most modern architectures work on 2D and it is difficult to
embed 3D knowledge. Secondly, neural networks are by design
over-parametrized and have millions or billions of parameters. It is
challenging to make the networks fast for high-resolution images and
videos, on mobile devices. Finally, neural networks are difficult to
debug and control as their behaviors are mostly governed by their
parameters and the training data.
On the other hand, classical graphics algorithms that explicitly model
the computation are less impacted by these issues, but they often do not
apply as broadly as modern data-driven methods. I will talk about my
research on connecting classical graphics algorithms with modern
data-driven methods, by making graphics algorithms differentiable to
enable optimization. This involves challenges in both algorithms and
systems. Many graphics algorithms, such as 3D rendering, involve
discontinuities and need to be taken care of when being differentiated.
On top of this, writing and deriving efficient derivative code for
graphics algorithms is tedious and error-prone. Deep learning frameworks
that are designed for a small number of high-throughput neural network
layers such as convolution or matrix multiplication are not sufficient
for complex graphics pipelines. We develop differentiable systems for 3D
rendering, image processing, and physical simulation that addresses
Tzu-Mao Li is a postdoc at the EECS department of UC Berkeley, working
with Jonathan Ragan-Kelley. His research focuses on the interactions
between three domains: visual computing, statistical learning, and
programming systems. He connects classical graphics and imaging
algorithms with modern data-driven methods to facilitate physical
understanding. He uses mathematical tools from statistics and machine
learning that broadly apply to graphics, vision, or even compiler
problems. He also develops programming systems that simplify the
efficient implementation and mathematical derivations of learnable
visual computing algorithms. He did his Ph.D. in the computer graphics
group at MIT CSAIL, advised by Frédo Durand. He received his B.S. and
M.S. degrees in computer science and information engineering from
National Taiwan University in 2011 and 2013, respectively. During his
time at National Taiwan University, he was a member of the graphics
group at Communication and Multimedia Lab, where he worked with Yung-Yu
Host: Professor Daniel Ritchie
Michael S. Goodman ’74 Memorial Seminar Series. Speaker:Juliet Davidow, Northeastern University. Title: Adolescent learning and goal-directed behavior. Abstract: Adolescence is a time of dynamic psychological and brain development. Previous research has shown that normative shifts in motivational processes during adolescence can relate to negative outcomes from risky actions, driven by biased interactions among the brain’s striatocortical circuitry. However, these brain systems support a range of functions, including learning and goal-directed action selection. Could motivational and neurodevelopmental change during adolescence confer learning advantages? My work highlights how development of multiple learning and control systems in the brain contributes to different aspects of behavior, associated with both benefits and costs in performance. Using fMRI and computational modeling approaches, my work has revealed that adolescents can engage in better learning strategies compared to adults, a behavioral profile that is supported by stronger interactions between the striatum and the hippocampus. However, when contextual demands change, stronger learning can become disadvantageous, such as when previous learning interacts with inhibitory control – a set of processes requiring interactions among late-developing lateral and medial prefrontal cortical regions. Taken together, this research illustrates both the advantages and challenges that arise from emerging functional orchestration of the brain during adolescence. This work contributes to a more complete characterization of this time of transition from childhood to adulthood.
CAAS Rounds presents:
Dopamine increases sensitivity to the benefits versus the costs of action: implications for cognitive effort and addiction by Andrew Westbrook PhD
Local and Global Uncertainty Representations – Implications for Dopamine Dysfunction in OCD by Andra Geana PhD
Feb2112:00pm164 Angell Street
Instructor: Rex Liu
This tutorial will provide a crash course on some of the basic methods in reinforcement learning. No prior knowledge beyond Python will be assumed. Emphasis will be on methods rather than proofs. We shall begin with Markov decision processes, the framework upon which all RL is formulated, followed by the central equation that RL essentially attempts to optimise, the Bellman equation. We shall then discuss how all learning methods attempt to optimise this equation, namely through policy evaluation, policy improvement, and value iteration. Finally, we shall cover one of the most important families of RL algorithms, TD-learning, and provide some hands-on exercises to play with these algorithms.
Carney Innovation Space, 4th Floor
164 Angell Street
Pizzas and sodas will be served. Sponsored by the Data Science Initiative and organized by the Center for Computation and Visualization.
Feb2012:00pmSidney E. Frank Hall for Life Sciences
Professor Marc Halushka from the Johns Hopkins University School of Medicine will present “Insights from the Gene Expression Tissue (GTEx) dataset”. This lecture is part of the 2020 Pathobiology Graduate Program Spring Seminar Series and all are welcome.
Feb2012:00pmBiomedical Center (BMC)
Speaker: Joao Passos, Mayo Clinic
The Biology of Aging Seminar Series brings to Brown some of the most renown scientists in the Biology of Aging field. Seminars are held once per month during the academic year, at noon on the third Thursday each month.
Feb194:00pm164 Angell Street
RUNS OF HOMOZYGOSITY, RECESSIVE DISEASE GENOTYPES, AND INBREEDING DEPRESSION IN DOMESTIC DOGS
SENIOR SCIENTIST, EMBARK VETERINARY INC.
Inbreeding leaves distinct genomic traces, most notably long genomic tracts that are identical by descent and completely homozygous. These runs of homozygosity (ROH) can contribute to inbreeding depression if they contain deleterious variants that are fully or partially recessive. The aim of this study was to examine the relationship between inbreeding measured from ROH the severity of inbreeding depression in several breeds for which phenotype data was available.
We examined genome-wide data from over 200,000 markers, which we used to build high-resolution ROH density maps for over 3,000 dogs, recording ROH down to 500 kilobases. Additionally, we utilized reproductive fitness-related phenotype data from the Morris Animal Foundation’s Golden
Retriever Lifetime Study, Doberman Pinscher longevity data from the Doberman Diversity Project, and other phenotype data collected from Embark Veterinary customers. We find that over the range of coefficient of inbreeding (COI) levels observed within several dog breeds, inbreeding depression is clearly evident using various types of phenotypic measures.
In most breeds, genetic testing to reduce the incidence of high COI litters will have a greater impact on animal welfare and population health than the current testing of known Mendelian disorders recommended by breed clubs.
Aaron Sams received his Master of Science in Biological Anthropology in 2010, as well as his Doctorate in Philosphy in 2012, both from the University of Wisconsin-Madison.
After spending almost four years post- graduation in Madison, Aaron went on to be a postdoctoral researcher at Cornell University before settling in at Embark, where he has been for almost four years.
He is an experienced interdisciplinary scientist with a background in biological anthropology, genomics, and computational biology. Aaron has a demonstrated history of working in the veterinary industry and uses computational methods to understand problem sin human evolutionary genomics. He is now applying those skills to canine genomics to help improve the lives of dogs.
Adapting black-box machine learning methods for causal inference
I’ll discuss the use of observational data to estimate the causal effect of a treatment on an outcome. This task is complicated by the presence of ‘confounders’ that influence both treatment and outcome, inducing observed associations that are not causal. Causal estimation is achieved by adjusting for this confounding by using observed covariate information. I’ll discuss the case where we observe covariates that carry sufficient information for the adjustment, but where explicit models relating treatment, outcome, covariates, and confounding are not available. For example, in medical data the covariates might consist of a large number of convenience health measurements of which only an unknown subset are relevant, and even then in some totally unknown manner. Or, the covariates might be a passage of (natural language) text that describes the relevant information. I’ll describe how to modify standard architectures and training objectives to achieve statistically efficient and practically useful causal estimates, as well as how to adapt traditional approaches to evaluating sensitivity to unobserved confounding to allow for the use of blackbox models.
Victor Veitch is a distinguished postdoctoral research scientist in the department of statistics at Columbia University. He completed his PhD in Statistics at the University of Toronto. His work addresses both the use of machine learning for causal inference, and the modeling of relational and network data. He has been recognized with a number of awards, including the 2017 Pierre Robillard award for best Statistics PhD thesis in Canada.
Michael S. Goodman ’74 Memorial Seminar Series. Speaker: Luke Chang, Dartmouth College. Title: Towards a computational social and affective neuroscience, Abstract: Emotions reflect coordinated, multi-system responses to events and situations relevant to survival and well-being. These responses emerge from appraisals of personal meaning that reference one’s goals, memories, internal body states, and beliefs about the world. Dysregulation of emotions is central to many brain and body-related disorders, making it of paramount importance to understand the neurobiological mechanisms that govern emotional experiences. Unfortunately, the field of emotion has struggled to reliably elicit and measure affective experiences, which has limited theoretical developments. One of the main focuses of our laboratory is to use a computational cognitive neuroscience framework to develop models of affective experiences. In this talk, I will provide two different examples of how we have been attempting to model emotions. First, I will present work that uses psychological game theory to model how emotions such as guilt can impact decision-making in social contexts. Second, I will present examples of how we can combine naturalistic elicitation of feelings with pattern-based neuroimaging analyses to develop brain-based models of affect. These models appear to generalize across individuals and can aid in revealing the temporal dynamics of individual affective experiences. We hope that this interdisciplinary work will aid in facilitating a more cumulative and extensible science of emotion.
Feb1412:00pm164 Angell Street
Data Science Computation and Visualization Workshop
EXPLORATORY DATA ANALYSIS WITH PANDAS IN PYTHON, PART ONE with Andras Zsom
Exploratory data analysis (EDA) is the first step of any data science project. In the first part of this pandas tutorial, I’ll walk through how to read in csv, excel, and sql data into a pandas data frame, how to select specific rows and columns based on index or condition, and how to merge and append various data frames. Coding experience with python is required but no experience with the pandas package is necessary to follow the tutorial.
Friday 2/14 @ 12pm
Carney Innovation Space, 4th Floor
164 Angell Street
Pizzas and sodas will be served. Sponsored by the Data Science Initiative and organized by the Center for Computation and Visualization.
Feb148:30am - 10:00am164 Angell StreetDo you have questions about data sharing, retention, curation, access, or management?As part of Love Data Week, please join the Carney Institute for a special Carney Coffee Hour and informal chat with data specialists from the Office of Research Integrity:Keri Godin, Senior Director of the Office of Research IntegrityAndrew Creamer, Scientific Data Management SpecialistArielle Nitenson, Senior Research Data Manager
Feb134:00pmSidney E. Frank Hall for Life Sciences
Chemistry of the Adaptive Mind, Lessons from Dopamine
Feb1312:00pmMetcalf Research Building
Michael S. Goodman ’74 Memorial Seminar Series. Speaker: Maruti Mishra, Harvard University. Title: The phenomenology of face Perception and its disorders. Abstract: In this talk, I will present two sets of studies on the phenomenology of face perception. One where I will discuss how attention can alter the phenomenology of facial expressions and the other where the ability to identify individuals by their faces (face blindness/prosopagnosia) is impaired.
Studies have shown that orienting attention to a spatial location not only improves performance but also enhances the appearance of low-level visual properties (like contrast, spatial frequency, etc.), thus altering phenomenology. But it is not known whether attention could similarly affect the perception of high-level visual information, like faces. Specifically, I investigated whether spatial attention could affect the appearance of facial emotional expressions? If so, then whether such modulation is distinct for different emotional valence? In such a scenario, would attention potentiate early or late neural modulation (ERP) of emotion-specific components?
In the second part of the talk, I will take you through various subjective and objective measures to identify a unique disorder (2% of the population), where people have life-long difficulty in recognizing faces -developmental prosopagnosia (DP). Often, researchers show that it is a disorder where people are unable to remember the face-identity information of familiar faces. But fresh findings from our current work show that there are ‘perceptual subtypes’ in DPs, that is people who also have face perception deficits along with memory deficits. I will discuss behavioral and neural differences between these two subtypes using ERP, fMRI and diffusion imaging analysis. I will also briefly discuss how our already available cognitive training procedure, that helps improve face recognition deficits in DPs, will benefit from the identification of these subtypes.
Feb115:30pm70 Ship Street
PAARF is a forum allowing students, postdocs, and junior faculty to present data in a friendly atmosphere with a focus on discussing unpublished research in progress. The objective is to stimulate a grass-roots dialogue not only to troubleshoot data from a variety of perspectives, but also to stimulate collaborations. PAARF is usually held on the 2nd Tuesday of the month at the Laboratories for Molecular Medicine at 70 Ship Street in room 107. Refreshments are served at 5:30pm and the presentations begin at 6:00pm.
Feb104:00pm - 6:00pm70 Ship Street
Feb101:00pm - 2:00pmWatson Center for Information Technology (CIT)Virtual reality (VR) offers new and compelling ways for users to interact with digital content. VR provides immersive experiences that can be beneficial in various applications, such as gaming, training simulations, education, communication, and design. As VR technologies continue to mature, and as commercial VR systems continue to grow in popularity, an opportunity exist to understand how to incorporate accessibility as a fundamental component in the design of VR systems. In this talk, I will describe ongoing research efforts to understand and eliminate accessibility barriers experienced by people with limited mobility when using VR systems. First, I will discuss the results of a study to identify challenges people with limited mobility encounter when using VR. Second, I will discuss approaches to make scene viewing in VR more accessible to people with limited or restricted movement in their head or neck. To conclude, I will discuss opportunities for future work to make VR more accessible and inclusive to people with a range of abilities.Martez Mott is a Postdoctoral Researcher in the Ability Group at Microsoft Research. In his research, Martez designs, implements, and evaluates intelligent interaction techniques to improve the accessibility of computer devices for people with limited mobility. Martez’s current research focuses on how to identify and overcome accessibility barriers embedded in the design of virtual and augmented reality systems. Martez received his Ph.D. in Information Science from the Information School at the University of Washington. Prior to attending UW, Martez received his B.S. and M.S. in Computer Science from Bowling Green State University.Host: Professor Jeff Huang
Michael S. Goodman ’74 Memorial Seminar Series. Speaker: Ishita Dasgupta, Harvard University. Title: Amortized inference in humans and machines: Algorithmic accounts of ecological rationality Abstract: How do humans reason intelligently in a complex and ever-changing world, with limited energy, data, and time? And why, at the same time, do they exhibit so many systematic biases and errors in their judgments and decisions? In this talk I will present new computational models that jointly explain both these remarkable successes and apparent failures of human reasoning. I argue that humans best use their limited resources by utilizing structure in their environment to cheaply approximate otherwise costly computations. I present models that can tractably learn these approximations by amortizing past computations. These approximations deviate systematically from the normative response when this environmental structure is violated, replicating several empirically observed cognitive biases. These “ecologically rational” cognitive models thereby resolve decades of conflicting findings about the rationality of human judgment. I will also talk briefly about how these insights can be used to build better and more human-like artificial intelligence.
Feb71:30pmWomen & Infants Hospital
Pediatric Research Colloquium
Xiaodi Chen, M.D. Ph.D.
Department of Pediatrics
Women & Infants Hospital of Rhode Island
“Human Pluripotent Stem Cells-Derived Brain Microvascular Endothelial Cells: Studying Blood-Brain Barrier in Developing Brain”
Light Refreshments will be served
Feb65:30pm - 6:30pmSmith-Buonanno Hall
The Data Science Initiative will be hosting an informational session discussing the new undergraduate Data Fluency Certificate on Thursday, Feb 6th from 5:30 – 6:30 p.m. in Smith Buonanno Hall, Room 101.
The certificate is designed to meet the learning goals of students interested in data science, without undertaking a CS or related concentration.
The certificate consists of three required courses, an upper level elective, and an experiential component. Current juniors, sophomores, and first year students are eligible for the Data Fluency Certificate. Students concentrating in any of the following (Computer Science, Applied Math, Computational Biology, Mathematics, or Statistics) (either jointly in combination) are not eligible.
Please join us for the information session to learn more about this exciting new data science opportunity at Brown!
Feb64:00pmSidney E. Frank Hall for Life Sciences
Neuroscience Graduate Program 2019-2020 Bench to Bedside Seminar Series
“Living with epilepsy around the clock”
Judy S. Liu, M.D., Ph.D.
Associate Professor of Neurology, Associate Professor of Molecular Biology, Cell Biology and Biochemistry Brown University
Christine Turenius-Bell, Ph.D.
Assistant Professor of Biology, Department Chair Community College of Rhode Island
Organized by the Brown University Center for Translational Neuroscience
Feb612:30pm - 1:30pm
This webinar is suitable for all those who are interested in working with patients suffering from Obsessive Compulsive Disorder (OCD). It reviews the assessment of OCD as well as evidence-based interventions such as psychoeducation, cognitive strategies, mindfulness, exposure and response prevention. We will focus on the integration of different treatment strategies for the different types of OCD symptoms. The impact of common comorbidities, strategies to address treatment non-adherence to maintain treatment gains will also be discussed.
- How to diagnose and assess OCD
- How to develop a personalized cognitive behavioral model with OCD patients
- How to tailor exposure and response prevention exercises, mindfulness skills and various cognitive strategies (e.g., continuum technique,) to different OCD symptom subtypes
- How to address pitfalls in treatment (reassurance seeking, low motivation), and how to prevent relapse
Physicians: The Warren Alpert Medical School of Brown University designates this live activity for a maximum of 1.0 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Psychologists: This program is sponsored by the Massachusetts Psychological Association. Massachusetts Psychological Association is approved by the American Psychological Association to sponsor continuing education for psychologists. Massachusetts Psychological Association maintains responsibility for this program and its content. This educational activity is designated for 1.0 Continuing Education Credits.
Physician Assistants & Nurse Practitioners: Participants will receive a Certificate of Attendance stating this program is designated for 1.0 hours AMA PRA Category 1 Credits™.
Please Note: In order to claim credit for your participation in this webinar, you will need to create an account with the accredited providers learning management system: https://cme-learning.brown.edu/.
Feb612:00pm164 Angell Street
Please join the Carney Institute for Brain Science for an informal lunch with Brown alumna Allison Paradise. Allison will talk about her career path and how her BASc in Neuroscience helped her found My Green Labs , a non-profit working to reduce waste in scientific research labs.
Please RSVP to help with the head count for catering.
Feb612:00pmMetcalf Research Building
Michael S. Goodman ’74 Memorial Seminar Series. Speaker: Nitzan Censor, TelAviv University. Title: Rapid long-term learning and unlearning in the human brain. Abstract: A plethora of studies have pointed to sensory plasticity in the adult visual system, documenting long-term improvements in perception. Such perceptual learning is enabled by repeated practice, inducing use-dependent plasticity in early visual areas and their readouts. I will discuss results from our lab challenging the fundamental assumption in perceptual learning that only ‘practice makes perfect’, indicating that brief reactivations of visual memories induce efficient rapid perceptual learning. Utilizing behavioral psychophysics, brain stimulation and neuroimaging, we aim to reveal the neurobehavioral mechanisms by which brief exposure to learned information modulates brain plasticity and supports rapid learning processes. In parallel, we investigate how these learning mechanisms operate across domains, for example by testing the hypothesis that similar inherent mechanisms may also result in maladaptive consequences, when brief reactivations occur spontaneously as intrusive enhanced memories following negative events. Unraveling the mechanisms of this new form of rapid learning could reshape learning theories across domains, setting the foundations to enhance learning in daily life when beneficial, and to downregulate maladaptive consequences of negative memories.
Feb512:00pmSidney E. Frank Hall for Life Sciences
Nonsense-mediated mRNA Decay is Mis-regulated in Fragile X Syndrome
Hosted by Kim Mowry
Feb511:00am - 12:30pm
Academic Grand Rounds* New Models of Stress and Trauma: Biology and Social Context in Times of TransitionButler Campus
Academic Grand Rounds*
New Models of Stress and Trauma: Biology and Social Context in Times of Transition
Nicole R. Nugent, PhD
Department of Psychiatry and Human Behavior
Department of Pediatrics
Department of Emergency Medicine
Alpert Brown Medical School
Director | RI Resilience Project
Director | Psychological Services at the Hasbro Pediatric Refugee Clinic
Associate Director | Stress Trauma and Resilience (STAR) Initiative
Wednesday, February 5, 2020
Butler Hospital ◊ Ray Hall Conference Center ◊ 11:00 am - 12:30 pm
Feb44:00pm164 Angell Street
EEG Core Initiative Seminar Series
Lauren Ostrowski and A. Nicole Dusang
(PIs: Leigh Hochberg, M.D. Ph.D. and David Lin, M.D.)
VA Center for Neuroestoration and Neurotechnology, Brown University, RI Massachusetts General Hospital, Boston, MA
” SAGEPrep: Semi-Automated GUI-based EEG Preprocessing”
“High Density EEG-Driven Arm Orthosis for Stroke Rehabilitation”
Coffee and cookies will be served. Please RSVP using the link below, as seating is limited.
Organized by the EEG Core Initiative, Brown University. Co-sponsored by the VA CfNN and the Carney Institute for Brain Science.
Host: Simona Temereanca , Ph.D.
SAGEPrep: Semi-Automated GUI-based EEG Preprocessing
EEG data are typically contaminated with artifacts (e.g., by eye movements, electrical contamination, and facial muscle movements), that must be removed from the data prior to analysis. Manual artifact rejection methods are largely time-consuming when applied to high-density or long-duration EEG data. Here, we present a semi-automated method to identify and delete temporal and spatial artifacts from EEG data. This pipeline utilizes parameters from the FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection) pipeline, with the addition of a user-friendly GUI and additional checkpoints for the reviewer to supervise artifact classification. Independent component analysis (ICA) is used to separate EEG data into neural activity and artifact. The semi-automated approach is particularly helpful with large datasets, and is designed to streamline manual review and target specific causes of artifact. Ongoing work explores how to incorporate core elements of our pre-processing pipeline into online brain-computer interface decoding.
High Density EEG-Driven Arm Orthosis for Stroke Rehabilitation
Approximately 800,000 individuals experience a first-time stroke in the United States each year. Eighty percent of strokes result in acute paresis of an upper extremity and the disease is a leading cause of long-term disability in adults. Options for upper extremity functional rehabilitation therapy remain limited, and better outcomes are desired by both patients and clinicians. Recent studies have demonstrated that the spontaneous recovery of upper extremity movement following stroke is influenced by identifiable neural circuits responsible for restoring various functions. For example, it has been shown that the ipsilesional premotor area (PMA) is a critical node in facilitating motor recovery, and connectivity measures between the ipsilesional primary motor area (M1) and PMA correlate with upper-extremity recovery. In the proposed research, we plan to target and harness these neural circuits with a brain-computer interface (BCI). BCIs are neurotechnologies which offer an opportunity to engage and reinforce select neural circuits for rehabilitation and recovery. Here, we will engineer a novel BCI that uses PMA-M1 connectivity to drive rehabilitation robotic arm. In the longer term, we intend to promote upper extremity recovery after stroke by strengthening PMA-M1 connectivity via Hebbian conditioning.
Feb34:30pm - 5:30pm
Talk: Show and Tell: Learning to Connect Images and Text for Natural Communication (Malihe Alikhani, Rutgers University)Watson Center for Information Technology (CIT)From the gestures that accompany speech to pictures in social media posts, humans effortlessly combine words with visual presentations. However, machines are not equipped to understand and generate such presentations due to people’s pervasive reliance on common-sense and world knowledge in relating words and pictures. I present a novel framework for modeling and learning a deeper combined understanding of text and images by classifying inferential relations to predict temporal, causal and logical entailments in context. This enables systems to make inferences with high accuracy while revealing author expectations and social-context preferences. I go on to design methods for generating text based on visual input that use these inferences to provide users with key requested information. The results show a dramatic improvement in the consistency and quality of the generated text by decreasing spurious information by half. Finally, I briefly sketch my other projects on multimodal and communicative systems and describe my research vision: to build human-level collaborative artificial intelligence entities by leveraging the cognitive science of language use.Host: Professor Ellie Pavlick
Feb312:00pmMetcalf Research Building
This talk has been cancelled and will be rescheduled.
Michael S. Goodman ’74 Memorial Seminar Series. Speaker, Fei Xu, UC, Berkeley. Title: The child as an active learner. Abstract: The child as an active learner’ has been an enduring theme in the study of developmental psychology. Yet what we mean by “an active learner” has been unclear. Here we investigate two sense of an active learner: (1) Can young children generate their own data to advance their own learning? And can former models help us understand the data generation process? (2) Do curiosity and interest facilitate learning in young children? I will present results from several studies to address these questions. Understanding the nature of active, self-directed learning may be an important step in developing a rational constructivist theory of cognitive development.
Michael S. Goodman ’74 Memorial Colloquium Series. Speaker: Dr. Michael Bernstein, Brown University. Title: Pious fraud or legitimate healing? An examination of the placebo effect. Abstract: In the early 19th century, Thomas Jefferson wrote: “One of the most successful physicians I have ever known has assured me that he used more bread pills, drops of colored water, and powders of hickory ashes, than of all other medicines put together. It was certainly a pious fraud.” Placebos are indeed ubiquitous in the history of medicine; in fact, they are still used today. Concerns of deception notwithstanding, calling placebos a fraud, as Jefferson did, implies they do not work. However, an emerging body of research shows that placebos are remarkably effective for certain conditions. In this talk, Dr. Bernstein will discuss the latest research on the placebo effect from a historical, medical, and psychological perspective. Recent trials suggesting that placebos work even when a patient knows they are taking one (open label placebos) will also be examined.
Jan304:00pmSidney E. Frank Hall for Life Sciences
Genome-wide in vivo CNS Screening Identifies Genes that Modify CNS Neuronal Survival and Mutant Huntingtin Toxicity