Brain Metabolism Study
The following study has been completed and the data is being used to develop new publications.
Advancing Personalized Antidepressant Treatment
Antidepressant treatment with selective serotonin reuptake inhibitors (SSRIs) is the most widely used treatment for Major Depressive Disorder (MDD). However, on average, SSRIs require six weeks for onset of action, and two-thirds of those on SSRIs fail to achieve remission.
Consequently, to reduce MDD morbidity and mortality, there is a critical need to improve our understanding of the neural signatures predictive of, and correlated with, an individual’s SSRI treatment outcome. Brain imaging has the potential to provide this insight.
In this study, we imaged 86 MDD subjects using a simultaneous PET/MRI scanner prior to and following 8 weeks of antidepressant treatment. Participants were randomized to either escitalopram (an SSRI) or placebo, allowing separation of SSRI-induced changes from the placebo effect.
Pretreatment images will allow the determination of a pretreatment marker of treatment effectiveness. Post to pre-treatment image comparison will allow analysis of treatment-induced brain changes and the correlation between these changes and clinical improvement.
Although the main foci of the study are (1) prediction of treatment effectiveness from pretreatment imaging and (2) relating image changes to mood changes following treatment, we collect a great deal of information on our participants and many imaging measures. This provides the data for many other potential examinations and publications.
Brain Imaging
We completed a brain imaging study sponsored by the National Institute of Mental Health (NIMH). The study involved brain imaging with Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI).
These imaging modalities allow us to gain a lot of information about the brain, including:
PET: We can assess metabolism of every brain region. This tells you what parts of the brain are very active compared to those that are not as active. It involves the injection of a compound called FDG, which is an analogue of sugar. So, when the brain takes up a lot of FDG, we know it is working hard and needs sugar for energy. The images we get from PET show us how much FDG is taken up in each brain region. See a sample image here.
structural MRI: Tells you the size and shape of every brain region. See an example here. (Different colors represent different brain regions.) We have automated techniques (e.g., Freesurfer) to extract volume/thickness of each region.
diffusion MRI: Allows us to measures the strength of connectivity between regions of the brain. Read more about it here.
Arterial Spin Labeling (also MRI): Measures the blood flow to every region of the brain. Like metabolism, this is another measure of how hard and how well each part of the brain is working. Read more about it here.
MRI spectroscopy: Measures metabolites like GABA and glutamate. Unlike the above modalities, spectroscopy is only performed in one region of the brain at a time. So, we don't get a whole brain image. Instead, we get measures of how much metabolite is in a particular region of the brain. In our case, we examine the anterior cingulate. A sample spectroscopy study can be found here.
This brain imaging is performed both before and after treatment.
Participant Questionnaires
Our participants completed the following surveys prior to treatment:
Adult Rejection Sensitivity Questionnaire
Childhood Trauma Questionnaire
Credibility And Expectancy Questionnaire
Edinburgh Handedness Inventory
Hamilton Depression Rating Scale (taken before, during and after treatment)
Inventory Of Depression And Anxiety
Multidimensional Scale Of Perceived Social Support
Quick Inventory Of Depressive Symptoms
Revised Social Anhedonia Scale
In addition, we also collect actigraphy, which measures the number of steps taken per day and number of hours of sleep a day (like Fitbit).
The following topics are currently being prepared for manuscripts by our students and interns (to give potential new interns a sense of how long it takes to complete a paper, I have included the time from joining the lab to publication for papers written by interns):
Farzana Ali (Stony Brook University): Use of PET and MRS with machine learning to predict antidepressant treatment response - NOW PUBLISHED! (graduate student)
Farzana Ali (Stony Brook University): Use of actigraphy to monitor and predict antidepressant treatment response - NOW PUBLISHED! (graduate student)
Samir Batheja (Half Hollow Hills High School East): Role of GABA/glutamate in depression with fatigue (joined lab as an intern in August 2020)
Mark Cherepashensky (Stony Brook University): Relationship between habenula metabolism and social anhedonia (began work as a VIP student in September 2019)
Daisy Dai (UC Berkeley): Use of spectroscopy to predict antidepressant efficacy (joined lab as an intern in January 2019)
Gabriel Davis (SUNY Downstate): Change in motor activity (as measured by actigraphy) with antidepressant response and correlation with neurobiology (joined lab as an intern in April 2020)
Brianna Donnelly (Hofstra University): Orbitofrontal Metabolism, volume, and thickness in relation to social anhedonia - NOW PUBLISHED! (joined lab as an intern in November 2019, paper accepted December 2023)
Samantha Goldstein (Stony Brook University): Neuroprotective effects of social support on white matter and brain volume in individuals who experienced early life stress (joined lab as an intern in September 2017)
Kathryn Hill (Stony Brook University): Use of FDG to predict antidepressant efficacy and assess effects of treatment - NOW PUBLISHED! (graduate student)
Joshua Jones (University of Rochester) and Samantha Goldstein (Stony Brook University): Evaluation of brain structure and function in depressed participants with childhood trauma - NOW PUBLISHED! (joined lab as an intern in January 2019, paper accepted September 2022)
Anjali Narayan and Kathryn Hill (Stony Brook University): Role of GABA and glutamate in antidepressant efficacy - NOW PUBLISHED! (joined lab as an intern in September 2018, paper accepted July 2022)
Karen Lin (Cornell University) and Vindhya Rapelli (Stony Brook University): Effects of obesity on amgydala and hippocampus volume changes with antidepressant treatment (began work as VIP students in January 2020)
Karen Lin (Cornell University) and Daniel Sunko (Stony Brook University): Relationship between hypothalamic- pituitary-adrenal (HPA) axis activity and hippocampus volume following antidepressant therapy (began work as VIP students in January 2020)
Yashar Yousefzadeh_fard (Stony Brook University): Neurobiology of expectancy and its impact on treatment efficacy (postdoctoral fellow)