Healing Families

Collaborators

David Dunson, PhD –Duke University

Bayesian statistical methodology motivated by complex biomedical data and machine learning applications. Ongoing methodologic research focuses on nonparametric Bayes, latent variable methods, variable and covariance selection in high dimensions, density regression, functional data analysis and mixture models. An emphasis is on developing adaptive nonparametric Bayes approaches for "learning" a low-dimensional structure underlying high-dimensional "objects" ( images, surfaces, shapes, text, array data, contingency table ). This work involves inter-discplinary thinking at the intersection of statistics, geometry and computer science. The motivation comes from applications in epidemiology, environmental health, neurosciences, brain-computer interfaces, genetics, and other settings.

Marc Caron, PhD –Duke University Medical Center

Dopamine, serotonin, and norepinephrine are key neurotransmitters in the central nervous system that regulates behavior and mobility. Studies of the mechanisms of action and regulation of these and other neurotransmitters and hormones at the cellular and molecular levels constitute the main goals of our research activities. Our laboratory uses a wide variety of techniques including animal models, cell systems, and molecular approaches to investigate how G protein-coupled receptors (GPCR) and neurotransmitter transporters regulate homeostasis in health and diseases


Nicole Calakos, MD PhD – Duke University Medical Center

We all know that as part of our daily lives we are constantly interacting with our environment - learning, adapting, establishing new memories and habits, and alas, forgetting as well. At the cellular level, these processes can be encoded by changes in the strength of synaptic transmission between neurons. The process by which neuronal connections change in response to experience is known as “synaptic plasticity” and this process is a major interest of our laboratory. Our goals are to understand the molecular mechanisms for synaptic plasticity and identify when these processes have gone awry in neurological diseases. In doing so, we will establish the necessary framework to then target these processes for therapeutic interventions; potentially identifying novel and improved treatment options. Currently, the lab is pursuing these questions in two areas.

Colleen Mcclung, PhD– University of Pittsburgh

The Laboratory of Dr. Colleen McClung is interested in discovering the molecular mechanisms of bipolar disorder, major depression and drug addiction.

There is a particular interest in studying the association between these various psychiatric disorders and the circadian clock.

The laboratory combines molecular and behavioral assays to determine more specifically how circadian rhythms and individual circadian genes regulate mood and addiction.


Bryan L. Roth, MD PhD –University of North Carolina Chapel Hill

The Roth Lab studies the structure and function of G-Protein coupled receptors (GPCRs). For more specific information check out our research page. The Roth lab is the principal contractor for the NIMH Psychoactive Drug Screening Program which includes the PDSP Ki database
For information about DREADDs visit our new Wiki page 

Akira Sawa, MD PhD – Johns Hopkins Medical School

The research in our laboratory is directed towards understanding the pathogenesis of neuropsychiatric illnesses, especially schizophrenia and neurodenerative disorders, at the molecular level.  Taking advantage of our roles in both basic and clinical departments, our approach is multi-faceted from molecular biology and animal models, to clinical studies using patient subject

Helen Mayberg, PhD – Emory University

Helen Mayberg, MD, has studied neural network models of mood regulation using neuroimaging for more than 20 years. Mayberg's research has led to the recent development of a new intervention for patients with severe depression. The intervention, known as deep brain stimulation, or DBS, is intended for those who have not had success with other treatments.

Yong-Hui Jiang, MD PhD – Duke University Medical Center

We are interested in understanding the genetic and epigenetic basis of neurodevelopmental disorders with emphasize on genomic imprinting disorders of Angelman syndrome and Prader-Willi syndrome as well as autism spectrum disorder. Angelman syndrome is caused by deficiency of brain-specific maternally expressed ubiquitin protein ligase 3A (UBE3A) genes. There was evidence supporting that HBII-85 SnoRNAs are responsible for the Prader-Willi syndrome. The genetic basis of autism spectrum disorder is largely unknown but mutations in several synaptic proteins including SHANK3 were reported in a small set of individuals with autism spectrum disorder. We are using cutting edge genome analysis techniques to identify genetic and epigenetic candidates for autism spectrum disorder. We have created mouse models using gene targeting and chromosomal engineering strategy for Angelman and Prader-Willi syndrome as well as autism. We are modeling these disorders in mice by application of biochemical, morphological, electrophysiological, and behavioral analyses. Finally, we are interested in exploring the potential of treating of Prader-Willi and Angelman syndrome by epigenetic modifications.

Miguel Nicolelis, MD PhD - Duke University Medical Center

A Brazilian physician and scientist, best known for his pioneering work in "reading monkey thought". He and his colleagues implanted electrode arrays into a monkey's brain that were able to detect the monkey's motor intent and thus able to control reaching and grasping movements performed by a robotic arm. This was possible by decoding signals of hundreds of neurons recorded in volitional areas of the cerebral cortex while the monkey played with a hand-held joystick to move a shape in a video game. These signals were sent to the robot arm, which then mimicked the monkey's movements and thus controlled the game. After a while the monkey realized that thinking about moving the shape was enough and it no longer needed to move the joystick. So it let go of the joystick and controlled the game purely through thought. A system in which brain signals directly control an artificial actuator is commonly referred to as brain-machine interface or brain-computer interface.