AAIC 2019 Alzheimer’s Imaging Consortium Talk on Amyloid in Down Syndrome

Individuals with Down syndrome (DS) have an increasing age-related prevalence of Alzheimer’s disease (AD). In DS, the triplication of amyloid precursor protein on chromosome 21 contributes to a life-long accumulation of brain amyloid. Dementia increases with age to over 75% prevalence after age 65 years.

In non-demented adults with DS, PET studies have shown increased amyloid uptake. However, the relationship between amyloid uptake and cognitive decline in DS has not been determined. This study compares brain amyloid distribution by consensus diagnosis in patients with DS using 18F-AV-45 PET.

The talk is available at the AAIC Learning Center and unfortunately is not free.

The poster and abstract

Or you can view the poster directly: DownsADDSPET_AAIC_2019

1RF1MH120021-01 Award Funded!

Featured

In this project we develop human neuroimaging domain-specific controlled vocabularies through community engagement and to provide tools for their use in BRAIN Initiative projects. The proposed work will provide a controlled vocabulary for use by the newer BRAIN Initiative projects, incorporating such annotations into the BIDS format and hosted through the BRAIN Initiative archives such as OpenNeuro. This project will greatly improve the ability to search across and reuse datasets.

Sign up for NIDM-Terms and stay tuned for more information about contributing!

Undergraduate Research Opportunities Program (UROP) Award to Nazek Queder

Congratulations to undergraduate student Nazek Queder who was awarded a research stipend to support her work on “Creating a New Brain Template for PET Studies of Alzheimer’s disease in the Down Syndrome Population,” under the supervision of Dr. David Keator.  Nazek will work on state-of-the art templates to improve our ability to understand regional amyloid accumulation in participants who are unable to tolerate an MRI scan.

Nazek is a 4th year psychology student with a broad experience in psychology, neuroscience, art, and programming.  Nazek’s passion is to help us understand how the brain works and is “thrilled to be able to contribute to society by us having more tangible measures to read and model brain atrophy in patients with Alzheimer’s Disease.”

A Semantic Cross-Species Derived Data Management Application

Managing dynamic information in large multi-site, multi-species, and multi-discipline consortia is a challenging task for data management applications. Often in academic research studies the goals for informatics teams are to build applications that provide extract-transform-load (ETL) functionality to archive and catalog source data that has been collected by the research teams. In consortia that cross species and methodological or scientific domains, building interfaces which supply data in a usable fashion and make intuitive sense to scientists from dramatically different backgrounds increases the complexity for developers. In this work we have built a multi-species data management system which uses semantic web techniques based on the Neuroimaging Data Model (NIDM ;Figure). We find this approach enables a low-cost, easy to maintain, and semantically meaningful information management system, enabling the diverse research teams to access and use the data.

Citation: Keator, D.B. et al., (2017). A Semantic Cross-Species Derived Data Management Application. Data Science Journal. 16, p.45. DOI: http://doi.org/10.5334/dsj-2017-045

Baseline 18F-AV-45 PET Predictors of Dementia Transition in Down’s Syndrome

Our work on brain-based biomarkers of dementia in Down’s Syndrome has been selected for an oral presentation at the AD/PDTM 2017, the 13th​ International Conference on Alzheimer’s and Parkinson’s Diseases and Related Neurological Disorders. In this work we show how amyloid burden in the brain as assessed with Positron Emission Tomography (PET) predicts future clinical transition to dementia.

Relationship between amyloid and increased risk of developing dementia in Down’s Syndrome.

Sharing brain mapping statistical results with the neuroimaging data model.

We’ve published a new paper using the Neuroimaging Data Model (NIDM) we’ve been developing for many years.  NIDM was started by an international team of cognitive scientists, computer scientists and statisticians, including PIs of this project, to develop a data format capable of describing all aspects of the data lifecycle, from raw data through analyses and provenance.  Our new published work shows how we’ve used NIDM to model mass univariate statistics in neuroimaging.

Maumet, C. et al. Sharing brain mapping statistical results with the neuroimaging data model. Sci. Data 3:160102 doi: 10.1038/sdata.2016.102 (2016).

For more NIDM info see: http://www.sciencedirect.com/science/article/pii/S105381191300596X

 

ReproNim: A Center for Reproducible Neuroimaging Computation

I am proud to announce this new P41 biotechnology research resource I am part of.   The Center for Reproducible Neuroimaging Computation, seeks to implement a shift in the way neuroimaging research is performed and reported. Through the development and implementation of a FAIR (Findable, Accessible, Interoperable and Reusable, Wilkinson et al., 2016) technology stack that supports a comprehensive set of data management, analysis, and utilization frameworks in support of both basic research and clinical activities, our overarching goal is to improve the reproducibility of neuroimaging science and extend the value of our national investment in neuroimaging research.

Stay tuned for interesting training materials and workshops on reproducibility!