Mood Brain & Development Unit (MBDU)
We Are Looking for a Post-Doc
We are trying to understand mood: the how and why of its fluctuations.
Candidates with a strong background in MRI or EEG/MEG as well as a keen interest in methodology and computational modeling will be most suited for the job. Advanced coding in R, Python, Matlab or Shell scripts is a requirement. Cognitive neuroscientists, engineers and other candidates with strong numerical and computational skills are particularly encouraged to apply.
For more information regarding this position: https://www.training.nih.gov/postdoc_jobs_nih/view/_31/5922/Post-Doctoral_Fellowship_at_The_Mood_Brain_and_Development_Unit_MBDU
Please write directly to Dr Stringaris: firstname.lastname@example.org
Congratulations to Dr. Stringaris and Kiana Khosravian for their awards in 2018! Dr. Stringaris won the 2018 NIMH IRP Outstanding Mentor Award ’intended to recognize outstanding mentorship by an NIMH investigator.’ Kiana Khosravian was awarded an NIMH IRP Trainee Travel Award to present her research in Rome.
Led by Argyris Stringaris MD, PhD, FRCPsych, the Mood Brain & Development Unit uses neuroimaging and computational methodology in order to improve the recognition and treatment of adolescents with depression. For this purpose, we have a clinical service that includes an inpatient unit dedicated to the research and treatment of adolescent depression and related conditions. The clinical service includes in-depth assessment as well as outpatient and inpatient treatment of depression.
The focus of our laboratory is on reward processing in adolescent depression, which we study using a range of methods: from computational modeling of behavior to neuroimaging methods such as functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG) and magnetoencephalography (MEG), and stimulation with Transcranial Magnetic Stimulation (TMS).
Methodologically, we try to combine the following:
Longitudinal designs: These help us to establish the direction of effects, an important part of trying to understand causes of depression in youth.
Computational approaches: These are being increasingly used as a means to derive parameters from models of our conception of psychological states, brain mechanisms and their interactions with the environment.
Neuroimaging: Using methods such as fMRI, EEG and MEG we are better able to understand neural mechanisms that may differ between people with and without depression in an effort to better understand how depression emerges in adolescence and test the impact of treatments.
Interventions: Treatments affect mood by changing brain mechanisms. Thus, they are important for assessing causal processes in people with depression. We use both pharmacological and psychological treatments with the goal of improving treatments for adolescent depression. We are also interested in the treatment potential of TMS.
Please see our Unit’s publications for more information.