Person:
Hollinshead, Marisa O.

Profile Picture

Email Address

AA Acceptance Date

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Hollinshead

First Name

Marisa O.

Name

Hollinshead, Marisa O.
Author's Publications: search.filters.isAuthorOfPublication.0a9e213f-b465-4aa1-807f-6101e0b60d13

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Dopamine Genetic Risk Score Predicts Depressive Symptoms in Healthy Adults and Adults with Depression
    (Public Library of Science, 2014) Pearson-Fuhrhop, Kristin M.; Dunn, Erin; Mortero, Sarah; Devan, William J.; Falcone, Guido J.; Lee, Phil; Holmes, A; Hollinshead, Marisa O.; Roffman, Joshua; Smoller, Jordan; Rosand, Jonathan; Cramer, Steven C.
    Background: Depression is a common source of human disability for which etiologic insights remain limited. Although abnormalities of monoamine neurotransmission, including dopamine, are theorized to contribute to the pathophysiology of depression, evidence linking dopamine-related genes to depression has been mixed. The current study sought to address this knowledge-gap by examining whether the combined effect of dopamine polymorphisms was associated with depressive symptomatology in both healthy individuals and individuals with depression. Methods: Data were drawn from three independent samples: (1) a discovery sample of healthy adult participants (n = 273); (2) a replication sample of adults with depression (n = 1,267); and (3) a replication sample of healthy adult participants (n = 382). A genetic risk score was created by combining functional polymorphisms from five genes involved in synaptic dopamine availability (COMT and DAT) and dopamine receptor binding (DRD1, DRD2, DRD3). Results: In the discovery sample, the genetic risk score was associated with depressive symptomatology (β = −0.80, p = 0.003), with lower dopamine genetic risk scores (indicating lower dopaminergic neurotransmission) predicting higher levels of depression. This result was replicated with a similar genetic risk score based on imputed genetic data from adults with depression (β = −0.51, p = 0.04). Results were of similar magnitude and in the expected direction in a cohort of healthy adult participants (β = −0.86, p = 0.15). Conclusions: Sequence variation in multiple genes regulating dopamine neurotransmission may influence depressive symptoms, in a manner that appears to be additive. Further studies are required to confirm the role of genetic variation in dopamine metabolism and depression.
  • Thumbnail Image
    Publication
    Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures
    (Nature Publishing Group, 2015) Holmes, Avram J.; Hollinshead, Marisa O.; O’Keefe, Timothy M.; Petrov, Victor I.; Fariello, Gabriele R.; Wald, Lawrence; Fischl, Bruce; Rosen, Bruce; Mair, Ross; Roffman, Joshua; Smoller, Jordan; Buckner, Randy
    The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset’s utility.